{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import h5py\n",
    "from lesion_extraction_2d.h5_query import get_lesion_info\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = 'h5/prostatex-train-ALL.hdf5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h5_file = h5py.File(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dicom_series_query(h5_file, query_words):\n",
    "    \"\"\"Returns a list of HDF5 groups of DICOM series that match words in query_words.\"\"\"\n",
    "    query_result = [\n",
    "        h5_file[patient_id][dcm_series]  # We want patients with DICOM series such that:\n",
    "        for patient_id in h5_file.keys()  # For all patients\n",
    "        for dcm_series in h5_file[patient_id].keys()  # For all DICOM series\n",
    "        for word in query_words  # For every word in query words\n",
    "        if word in dcm_series  # The word is present in DICOM series name\n",
    "        ]\n",
    "    return query_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filename_to_patient_id(name):\n",
    "    return name[11:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_lesion_info(h5_file, query_words):\n",
    "    query = dicom_series_query(h5_file, query_words)\n",
    "\n",
    "    # list of attributes to include in the lesion info\n",
    "    include_attrs = ['ijk', 'VoxelSpacing', 'Zone', 'ClinSig']\n",
    "\n",
    "    lesions_info = []\n",
    "    for h5_group in query:\n",
    "        pixel_array = h5_group['pixel_array'][:]  # The actual DICOM pixel data\n",
    "        patient_age = h5_group['pixel_array'].attrs.get('Age')\n",
    "\n",
    "        lesion_info = []\n",
    "        for finding_id in h5_group['lesions'].keys():\n",
    "            lesion_dict = {\n",
    "                'name': h5_group.name,\n",
    "                'patient_id': filename_to_patient_id(h5_group.name)\n",
    "            }\n",
    "            for attr in include_attrs:\n",
    "                # Per lesion finding, gather the attributes necessary for actual lesion extraction from DICOM image\n",
    "                lesion_dict[attr] = h5_group['lesions'][finding_id].attrs.get(attr)\n",
    "            lesion_dict['fid'] = finding_id\n",
    "            lesion_dict['Age'] = patient_age\n",
    "            lesion_info.append(lesion_dict)\n",
    "\n",
    "        lesions_info.append([lesion_info, pixel_array])\n",
    "\n",
    "    return lesions_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Centroid:\n",
    "    def __init__(self, x, y, z):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.z = z\n",
    "\n",
    "    def __repr__(self):\n",
    "        return '({}, {}, {})'.format(self.x, self.y, self.z)\n",
    "\n",
    "\n",
    "def extract_lesion_2d(img, centroid_position, size=None, realsize=10, imagetype='ADC'):\n",
    "    if imagetype == 'ADC':\n",
    "        if size is None:\n",
    "            sizecal = math.ceil(realsize / 1.5)\n",
    "        else:\n",
    "            sizecal = size\n",
    "    else:\n",
    "        sizecal = size\n",
    "    x_start = int(centroid_position.x - sizecal / 2)\n",
    "    x_end = int(centroid_position.x + sizecal / 2)\n",
    "    y_start = int(centroid_position.y - sizecal / 2)\n",
    "    y_end = int(centroid_position.y + sizecal / 2)\n",
    "\n",
    "    if centroid_position.z < 0 or centroid_position.z >= len(img):\n",
    "        return None\n",
    "\n",
    "    img_slice = img[centroid_position.z]\n",
    "\n",
    "    return img_slice[y_start:y_end, x_start:x_end]\n",
    "\n",
    "\n",
    "def parse_centroid(ijk):\n",
    "    coordinates = ijk.split(b\" \")\n",
    "    return Centroid(int(coordinates[0]), int(coordinates[1]), int(coordinates[2]))\n",
    "\n",
    "\n",
    "def get_train_data(h5_file, query_words, size_px=12):\n",
    "    lesion_info = get_lesion_info(h5_file, query_words)\n",
    "\n",
    "    X = []\n",
    "    y = []\n",
    "    lesion_attributes = []\n",
    "    for infos, image in lesion_info:\n",
    "        for lesion in infos:\n",
    "\n",
    "            centroid = parse_centroid(lesion['ijk'])\n",
    "            lesion_img = extract_lesion_2d(image, centroid, size=size_px)\n",
    "            if lesion_img is None:\n",
    "                continue\n",
    "\n",
    "            X.append(lesion_img)\n",
    "\n",
    "            lesion_attributes.append(lesion)\n",
    "\n",
    "            y.append(lesion['ClinSig'] == b\"TRUE\")\n",
    "\n",
    "    return np.asarray(X), np.asarray(y), np.asarray(lesion_attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X, y, attr = get_train_data(h5_file, ['Ktrans'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 7, 19, 20, 23, 24, 32, 34, 38, 44, 47, 53, 56, 61, 70, 72, 86, 95, 101, 102, 106, 124, 128, 129, 132, 134, 141, 142, 144, 146, 149, 157, 160, 162, 164, 166, 168, 172, 175, 177, 179, 180, 181, 184, 188, 204, 215, 223, 252, 258, 278, 279, 281, 283, 285, 287, 289, 296, 299, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 316, 317, 319, 320]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "75"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_t = 0\n",
    "true_lst = []\n",
    "for item in attr:\n",
    "    if item['ClinSig'] == 'TRUE':\n",
    "        true_lst.append(i_t)\n",
    "    i_t += 1\n",
    "print true_lst\n",
    "len(true_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 25, 26, 27, 28, 29, 30, 31, 33, 35, 36, 37, 39, 40, 41, 42, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 103, 104, 105, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 125, 126, 127, 130, 131, 133, 135, 136, 137, 138, 139, 140, 143, 145, 147, 148, 150, 151, 152, 153, 154, 155, 156, 158, 159, 161, 163, 165, 167, 169, 170, 171, 173, 174, 176, 178, 182, 183, 185, 186, 187, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218, 219, 220, 221, 222, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 253, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 280, 282, 284, 286, 288, 290, 291, 292, 293, 294, 295, 297, 298, 300, 301, 302, 303, 315, 318]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "246"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_t = 0\n",
    "false_lst = []\n",
    "for item in attr:\n",
    "    if item['ClinSig'] == 'FALSE':\n",
    "        false_lst.append(i_t)\n",
    "    i_t += 1\n",
    "print false_lst\n",
    "len(false_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bktb3PL+4+1qpun8K7ZT0REQ8Xfb2JV0r6Wu2j0j6L0n/ZPtnFdQ5LulYRLzUfb5TnaCX\n6UZJRyLiLxExLekpSdeUXKPXhO0LJcn2JyW9U2GtutSSa4lsJ6oz27Xkuomm/TtJn7a9oXsW93ZJ\nVZyd/qmkAxHxcAXbVkR8PyLWR8Sn1PkedkfEnRXUmZB0zPbm7ks7VP7JoaOSvmR73J3PJ+9QuSeE\nzt1b2yXpru7X35RUReOpW125lsh2iiqz3UyuI6L2h6SbJR2SdFjSAxVs/1pJ05L2SXpZ0l5JN1f4\n/VwvaVeF2/+cOk1hnzp7CisrqPEDdcK8X52TKGMlbffn6pz4eU+dX6C7Ja2W9Fw3A89KWlVn/ir8\nOVWa624Nsp1eo/RsN5lrPsYOABkZ6hORADBsaNoAkBGaNgBkhKYNABmhaQNARmjaAJARmjYAZISm\nDQAZ+X/nq1NYSyX0VgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce7d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''normali the image data'''\n",
    "out_np = X[0]\n",
    "X_out = (out_np - out_np.flatten().min())/(out_np.flatten().max() - out_np.flatten().min())\n",
    "plt.subplot(1,2,1)\n",
    "# plt.hist(X_out.flatten(), 100)\n",
    "plt.imshow(X_out, cmap='gray', interpolation='none')\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(out_np, cmap='gray', interpolation='none')\n",
    "np.max(X_out)\n",
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_lst = []\n",
    "for image in X:\n",
    "    X_out = (image - image.flatten().min())/(image.flatten().max() - image.flatten().min())\n",
    "    X_lst.append(X_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5SKXbZRB/Xjx1o9MseTv1dcAjwMmExRkvJBUaTT/XyngYG9sg05lEpNLtMog/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\nZxLx/wEEwKUXmUooPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce7d310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in true_lst[:10]:\n",
    "    plt.subplot(5, 2,true_lst.index(item)+1)\n",
    "    plt.hist(X_lst[item].flatten(),100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "window = [0.1, 0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jYl+7ArPiuF37m6RbJI1K2lrx2lpJu7KFqA8sRm0JyjuksSF5+tEbOTZvl86yZctqls+c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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f317d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_num = 0\n",
    "window_img = np.clip(X_lst[img_num], window[0], window[1])\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(X_lst[img_num], cmap='gray', interpolation='none')\n",
    "plt.subplot(1,3, 2)\n",
    "plt.imshow(window_img, cmap='gray', interpolation='none')\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.hist(X_lst[img_num].flatten(), 100)\n",
    "img_num in true_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EzEQ2ATORTcBMZNM7z5P806dPq6KiQkeOHNGxY8dUV1cX8/P7OrJ333036BaQosim3dtv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\nNsklgpCsc1on3//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1zZ/fvn27tXfvXquoqKi59sQTT1jLli2zLMuyli5dai1evDhqH4Bp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\n2QTMRDZbuvq4+Q//8A9Rx3qe5N9yyy0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lZGQ4btOp/hd/8Re22ptvvmmrLV++3FY7fvy4\nrTZjxgytX7++w06aYK5YstnU1NSilp4e3x08V/7iuczpl/KXv/xlW+3ywirJkJaWRjaRdMk4bkZ7\nvFDPnj1ttW7dutlqgwcPttX27NnjuM2f/vSntlpWVpattnfvXlvNaeFOqeP+oRFmS9Y5bXtGNhGE\noLL58MMPO9Z/9KMf+bqfeLV2Puv5nfwxY8aosbHRc1MAEoNsAmYim4CZyCZgJrLpnS+r6wMAAAAA\ngOAxyQcAAAAAoIOIe5Lf1NSkkpISTZkyxY9+APiEbAJmIpuAmcgmYCayGbu4VteXpFWrVqmwsFBn\nzpyJ6Wuutn37dsexTgt0nT9/3labMGGC6/27VVJSYqs9+uijtprTgkZdu3b1vR8gFm6y2alT3L8C\nWnB6rEePHj1stVR/BApSm5fjptPisGfPnvWzraiGDRvmWL/22mttNacFa53+ndEW3gOC5CWbAwYM\nsNWc8vrJJ584fv2pU6fcNwikKC/ZjMePf/xjx7ppC++1Jq538qurq7V582Y99NBDfvUDwAdkEzAT\n2QTMRDYBM5FNb+Ka5C9cuFDLly9P6UeKACYim4CZyCZgJrIJmIlseuP5Wt1NmzYpFAqpuLhYkUiE\n52dKamxsbH7Mg9NzgYFkiCWbTU1NzR935F+ekUjE8VYCIJk4bgJmIpuAmchmS7Gcz3qe5O/cuVMb\nN27U5s2bdeHCBZ09e1azZs3SSy+95HWT7V5GRoYyMjIkSSNGjNAf/vCHgDtCKoolm+npLS/m6ai/\nPMPhsMLhcPPr8vLy4JpByuK4CZiJbAJmIpstxXI+6/ly/SVLlujo0aM6ePCgNmzYoPHjx6fsNxww\nCdkEzEQ2ATORTcBMZNM7f5fWjuLyJeyX7dy50zbm29/+tuPXjhw50lYrLi621ZJ1qbHTfqKtcLxm\nzZoEdwPE58rL9f3w3e9+11YrKyuz1TryrQFAIjgdZ5KVo2hX+Hz66ae22g033GCrnT592veegKAs\nXry4xeunn37a9304Ze7ZZ5+11Z544gnf9w0g+nHv6itgJf/Ppf3iyyR/3LhxGjdunB+bAuAjsgmY\niWwCZiKbgJnIZmziWl0fAAAAAACYI65Jfm1traZPn66CggINGTJEe/bs8asvAHEgm4CZyCZgJrIJ\nmIlsehPX5foLFizQ1772Nf3yl79UQ0ODzp8/71dfAOJANgEzkU3ATGQTMBPZ9CbN8vjMrDNnzmjE\niBE6cOBA6ztIS7MtSDBhwgTbuG3btjl+/Z133mmrPf/887ZaZmZmq334xenbtXz5clutoKBAU6ZM\n6bCPJIO5YslmPJzui3rllVdsteuuu873fccrLS2NbCLp4slmkD+v0fb9V3/1V7bawIEDbbVf//rX\nttrhw4dj2heQSPGc0wa5AKZT7fKjnJOxfyDRknFOO3jwYFutW7dujmN///vf22qXLl2y1bp06WKr\nde3a1XGbn3/+ua22adMmW23MmDG2Wp8+faIvEuhYdeHQoUPq27ev5syZo5KSEs2fP18XLlzwujkA\nPiGbgJnIJmAmsgmYiWx65/ly/YaGBlVVVem5557TqFGj9Oijj2rp0qUqLy+3jb3yEVrhcNjrLo13\n4MABHTx4UJK0d+/egLtBqoolm6kiEokoEokE3QZSHNkEzBTPOe3tt9+exE6B1MJxs6UdO3Zox44d\nrsZ6nuT3799f2dnZGjVqlCSptLRUy5Ytcxx79XOyf/CDH3jdrdEGDhzYfKliQUGBNmzYEHBHSEWx\nZDNVhMPhFn9gTNWDA4JFNgEzxXNOCyBxOG62NHbsWI0dO7b59T//8z9HHev5cv1QKKTs7Gzt379f\nklRZWanCwkKvmwPgE7IJmIlsAmYim4CZyKZ3ca2uv3r1as2cOVOXLl1Sbm6uXnzxRcdx6enen9Tn\ntJDCDTfc4Hl7iZCfn2+rJWshQMCJ22zGY8SIEbbaJ598Yqv17dvX930D7ZXbbF69uJeJ5s2bZ6uV\nlJTYauvXr09GO0Bc3GYzqIVj3e63X79+jvXjx4/72Q6QNH6e0zotqNejRw9bLVqOBgwYYKvV1NTY\nanPnzrXVnBbok+KbJ7cmrkn+8OHD9fbbb/vVCwCfkE3ATGQTMBPZBMxENr1JzJ8OAAAAAABA0jHJ\nBwAAAACgg4hrkr9y5UoNHTpUw4YN08yZM1VfX+9XXwDiQDYBM5FNwExkEzAT2fQmzbIsy8sXHjt2\nTGPHjtX777+vLl266K//+q/1V3/1V5o1a1bLHcS5QMnmzZttta9+9atxbTMeTt+uX//617ZaVlaW\nRo4c6TgeSKRkZbO0tNRWu/fee221Bx54IK79JEJaWhrZRNLFks328PPptsd//Md/tNW+//3vx7VN\nwE/tNZtOvcyZM8dx7Nq1a33fF5BoyTqnbc+iZTOuhfcaGxt17tw5paen6/z586woDxiCbAJmIpuA\nmcgmYCay6Y3ny/UzMzO1aNEi5eTkKCsrS71799bEiRP97A2AB2QTMBPZBMxENoH/z979R0Vx3/vj\nfwL+ilI1xrgIAhUjP+VnNKbBxA0atDkVGy+mIVapmtqea3usV6/mJqcn0HNjoBqtpjf3tmkM2lRp\n7O0J3vojvRI3VfwRDRrxJtGkKgYUSFJBUZEA7+8fft2P68zuzs7O7rzZeT7OySn74s3MC8uTmTc7\n8x45MZv66X4nv7W1FVVVVaivr8eQIUNQWFiILVu2SHlpbrDU1dWhrq4OADB48GCTuyGrYjaVHA4H\nHA6H2W2QxfmSzZKSEufHdrsddrs9eI0SWQyzSSQnntPqp3uSv2fPHiQkJGDYsGEAgFmzZuHAgQOW\n/kdPT09Heno6gJv35P/2t781uSOyImZT6c4TsdLSUvOaIcvyJZu3TySIKLCYTSI58ZxWP92X68fF\nxeHQoUPo6OiAEALV1dVISUkxsjci0oHZJJITs0kkJ2aTSE7Mpn6638l/4IEHUFhYiOzsbPTt2xfZ\n2dlYtGiR6tju7m6X13//+98VYz788EPVr83Pz9fbYtCsWrVKUXvsscdM6ITIt2z6409/+pOidu+9\n9ypq586dU9See+45w/shkp3R2VRbUTc8XP1v92orD7/++uuKWnx8vKKWl5eneZtqPdlsNkVN7bjJ\n3wtkFl+yuXfvXpfXjz76aDBa1OzkyZNmt0BkmGCd04Yiv1bXf+GFF/DCCy8Y1QsRGYTZJJITs0kk\nJ2aTSE7Mpj66L9cnIiIiIiIiIrl4neQvXLgQNpsNGRkZztqlS5eQn5+PpKQkTJs2DW1tbQFtkoiU\nmE0iOTGbRHJiNonkxGwaz+skf/78+XjnnXdcamVlZZg6dSpOnTqFvLw8vPTSSwFrkIjUMZtEcmI2\nieTEbBLJidk0XphQWyXnDvX19ZgxYwZOnDgBAEhOTsZ7770Hm82GpqYm2O12fPLJJ+o7CAtTLMSj\nYZeem1ZZ7CcQ1PrMzs5W1NQWDSwqKsLWrVv9/l6JPPE3mxs2bHCpXbhwQTFu3Lhxql//2muvKWr9\n+/dX1BISEhS16OhoRe3nP/+56n4CQe33EpGRgnHcvP0dj1t8WXRL7Vh650K57sa5o9ZnbW2tolZQ\nUKCoXbhwgbmkgPM3mz/5yU9camqLSH7jG98wvG+1bKg9qvngwYOqX79p0ybD909kJH+zKRO1Y9zb\nb7+tOlYtWxEREZr35S6buu7Jb2lpca6WGxUVhZaWFj2bISKDMZtEcmI2ieTEbBLJidn0j1+r69/i\n7a8nJSUlzo/tdjsmT55sxG6lVldXZ3YLRF6zuXPnTufHY8eOxaBBgwLdkikcDgccDofZbRA58bgJ\n3LhxAzdu3DC7DSIX3rJ5+PBh58cxMTGBboeI/n+yvVsvO12TfJvNhubmZuflEyNGjPA4/vaTFcAa\nl/ykp6fzWaUUdL5m8/HHH3d5rXa5fiiw2+2w2+3O16WlpeY1Q5bE46ZS//79XW7xaW9vN7Ebsipf\nszlx4sQgdUZkbb5mk1xpulxfCOFyglFQUICKigoAN+/xmTlzZkCaIyLPmE0iOTGbRHJiNonkxGwa\ny+vCe08//TQcDge++uor2Gw2lJaW4rvf/S5mz56Nzz//HPHx8XjrrbcwdOhQ9R1IuMCVWj+3Fnm4\n3fHjxxW1xsZGRe35559X1LjwHgWaEdns6enRvX+tC4GpXV710UcfKWpr165V3c+SJUt0dOeZjL+X\nKHQE4rip9vMaHq5rWR2n7du3K2ozZszQ/PVqPe3du1dRO3/+vKKmtmDtr371K+aSAsqIbN658N6s\nWbMU4x599FG/+lTLwenTpxW1ZcuWKWo7duzwa9++9ERkFCOyaZb4+HhF7cyZM4qaL4vp+cJdNr1e\nrr9lyxbV+p49e3xuwuFwuFwyG6xxvow9cuQIJkyYoGmbZ86cUV05nCgYjMpmIHJ09epVzff3CyE0\n/XIORJ9EgWBGNgMhEMfiTz75BMnJyf41RqSTUdlsaGjAqFGjNI0NRI4OHz7M2wYopBg53zRLR0cH\nBgwYYHYbTv69DeAjrQtfGT3Ol7FHjx7VvM2zZ89qHkskq0Dk6OrVq5q3qfXdgUD0SSQzs3+OA3Es\ndvf4I6LeRO2qTncCkaP3339f81giCo6Ojg6zW3AR1Ek+EREREREREQUOJ/lEREREREREIcLrwnt+\n78DizzTkQiUkK2aT2SQ5WTmbzCXJjNkkkhOzqRTwST4RERERERERBQcv1yciIiIiIiIKEZzkExER\nEREREYUITvKJiIiIiIiIQkRQJvm7d+9GcnIyEhMTUV5e7nZcQ0MD8vLykJaWhvT0dGzYsMHjdnt6\nepCTk4OCggK3Y9ra2jB79mykpKQgLS0Nhw8fdjt23bp1GDduHDIyMjBnzhx0dnY6P7dw4ULYbDZk\nZGQ4a5cuXUJ+fj6SkpIwbdo0tLW1eeyXSDZasulrLoHgZZO5pFDFbBLJKRDZ1JJLQHs2eT5LVsRs\n3kEEWHd3txgzZow4d+6c6OzsFJmZmeLjjz9WHXvx4kVx7NgxIYQQV65cEYmJiW7HCiHE2rVrxZw5\nc8SMGTPcjikuLhYbN24UQgjx9ddfi7a2NtVxjY2NYvTo0eLGjRtCCCGefPJJsWnTJufn9+3bJ44d\nOybS09OdtRUrVojy8nIhhBBlZWVi5cqVbvsgko3WbPqaSyGCl03mkkIRs0kkp0BlU0suhdCWTZ7P\nkhUxm0oBfyf//fffx9ixYxEfH4++ffviqaeeQlVVlerYqKgoZGVlAQAiIyORkpKCxsZG1bENDQ3Y\nuXMnnnnmGbf7vnz5Mvbt24f58+cDAPr06YPBgwe7Hd/d3Y2rV6+iq6sL165dQ3R0tPNzkyZNwt13\n3+0yvqqqCsXFxQCA4uJivP322263TSQbrdn0JZdAcLPJXFIoYjaJ5BSIbGrJJeBbNnk+S1bDbCoF\nfJLf2NiI2NhY5+tRo0Z5PAm55dy5czh+/DgmTpyo+vmlS5di9erVHp+LePbsWQwfPhzz589HTk4O\nFi1ahOvXr6uOjY6OxrJlyxAXF4eYmBgMHToUU6dO9dhjS0sLbDYbgJs/NC0tLV6/LyJZ6Mmmt1wC\n5meTuaTejtkkklMgsqkll4D2bPJ8lqyI2VSScuG99vZ2FBYWYv369YiMjFR8fseOHbDZbMjKyoIQ\nAkII1e10dXWhtrYWixcvRm1tLQYOHIiysjLVsa2traiqqkJ9fT0uXLiA9vZ2bNmyxae+vf0QEPVm\n3nIJyJlN5pJCHbNJJCejzmcB7dnk+SyRd1bIZsAn+TExMTh//rzzdUNDA2JiYtyO7+rqQmFhIebO\nnYuZM2eqjqmpqcH27duRkJCAoqIi7N27F/PmzVOMGzVqFGJjYzF+/HgAQGFhIWpra1W3uWfPHiQk\nJGDYsGGIiIjArFmzcODAAY/fm81mQ3NzMwCgqakJI0aM8DieSCa+ZFNLLgE5sslcUm/HbBLJyehs\nas0loD2bPJ8lK2I2lfya5GtZxXDChAn47LPPUF9fj87OTlRWVnpcoXDBggVITU3FkiVL3I5ZtWoV\nzp8/jzNnzqCyshJ5eXnYvHmzYpzNZkNsbCxOnz4NAKiurkZqaqrqNuPi4nDo0CF0dHRACIHq6mqk\npKS4jLnzLzkFBQWoqKgAAGzatMnjCRZRMBmdTS25BMzJJnNJvQmzWQGA2ST5mJFNrbkEtGeT57MU\napjNCgA6sql5ib47+LJq/q5du0RiYqK47777xEsvveR2m/v37xfh4eEiMzNTZGVliezsbLFr1y6P\nfTgcDo8rHh4/flyMHz9eZGZmiieeeEK0tra6HVtSUiKSk5NFenq6mDdvnujs7HR+rqioSIwcOVL0\n69dPxMbGio0bN4p//OMfYsqUKSIxMVE89thj4tKlSx57JQoGo7OpJ5dCBCebzCX1Jswms0lykiGb\n3nIphPZs8nyWQgWzqT+bYUJ4uMnAg0OHDqG0tBS7du0CAJSVlSEsLAwrV650GWf1+3p0/vMS6cZs\nasNsUrAxm94xl2QGZtM7ZpPMwGx65y6bffRuUG0Vw/fff1/v5kJOUVERtm7danYbZEG9IZvnzp1z\nfrxu3TosXbrU5TEit+vXr5+h+16+fDnWrFlj6DaJtOgN2fTXrUcI3W7jxo3Oj0tKSlBSUqL6tVY+\nSSNz9dZsqt1zPGTIEOfHLS0tznt4P/roo6D1RWQUX7L55Zdfory83PkHgHvuucfjtm8dj9QmyaNH\nj3Z+3NraiqFDhwIA6uvrNfU9adIkRe0nP/mJ8+Nt27Zh9uzZAIC2tjbF2EWLFrn06I6n46buST55\nVldXZ3YLRKSipqbG7BaICIDD4YDD4TC7DSIiCgHl5eWoqalBeXk5cnNzQ3JtCV+Om7on+b6umm81\n6enpOHnypNltkAUxm57l5ubi4MGDZrdBFsRsurLb7bDb7c7XpaWl5jVDlsZsEsnJl2yuXLnS5Z38\nUOTLcVP36vq+rppPRMHR27L54IMPmt0CUVD0tmwGwu0nJ0SyCNVsDho0yOwWiPziazZzc3M1b1vr\n8WjAgAGat6mVu6fW3MmfY6bud/IjIiLw61//Gvn5+ejp6cHChQsVjwEIlJycHEXt6NGjipq7hQje\nfPNNRa24uFjTvp966ilFrbKyUtPXEgWDmdl8+eWXFbWlS5d6/Jq4uDiPn+/u7lbUIiIifGuMSAJm\nZjMQ+vRRnkK8/vrrHr+Gk3ySUW/NZmNjo6J257lva2trsNohMpwv2dy5c6fL/86dO9fjtm8dj9Tu\naz9z5ozq1+zevVtRU5sbTpgwQVG7/Y8F999/v/PjFStWKMa++OKLipramjee+HVP/vTp03Hq1Cl/\nNkFEAcBsEsmJ2SSSE7NJJCdmUx/dl+sTERERERERkVw4ySciIiIiIiIKEbon+Q0NDcjLy0NaWhrS\n09OxYcMGI/siIp2YTSI5MZtEcmI2ieTEbOoXJtytTudFU1MTmpqakJWVhfb2dtx///2oqqpCcnKy\n6w5UFjMYNWqUouZuIa3Nmzcrag8//LCiprYfd9S+5S+++EJR+6//+i9F7YUXXtC0j6KiImzdutXt\n4n9EgeJPNn1x6NAhRe2BBx5Q1Pzdj5qenh5FTetifMuXL8eaNWuYTQq6YGXTaHfddZdq/erVq4qa\nP72HhYUxl2SK3prNYGI2yQy+ZPPOfKqdK/rLnxyoLZQJqC9ArbafO79nAPjkk0/c9qT7nfyoqChk\nZWUBACIjI5GSkuK2eSIKHmaTSE7MJpGcmE0iOTGb+hlyT/65c+dw/PhxTJw40YjNEZFBmE0iOTGb\nRHJiNonkxGz6xq9H6AFAe3s7CgsLsX79ekRGRhrRU0ioq6szuwWyOGZTXU1NjdktkMUxmzc5HA44\nHA6z2yByYjaJ5KQlm1a4peTq1au4du2aprF+TfK7urpQWFiIuXPnYubMmf5sKuSkp6fj5MmTZrdB\nFsVsupebm4uDBw+a3QZZFLP5/9jtdtjtdufr0tJS85ohy2M2ieSkNZtWWDNj0KBBGDRokPP1V199\n5XasX5P8BQsWIDU1FUuWLPHp6xoaGjSPHTJkiKLm7/+Jal9/7733Kmrf/OY3FbXY2FhF7fPPP/er\nHyKj6c2mmkmTJqnWg7XInhor/CKn0GRkNoNlxIgRqnXmkEJJb8wmkRVozebu3bsD3os/x72YmBjV\neldXl6Kmtpi0L/NnwI978mtqavCHP/wB7777LrKzs5GTkxOUf1wi8ozZJJITs0kkJ2aTSE7Mpn66\n38nPzc1Fd3e3kb0QkQGYTSI5MZtEcmI2ieTEbOpnyOr6RERERERERGQ+vyf5PT09yMnJQUFBgRH9\nEJFBmE0iOTGbRHJiNonkxGz6zu9J/vr165GammpEL0RkIGaTSE7MJpGcmE0iOTGbvvNrdf2Ghgbs\n3LkTzz//PNauXetXI//+7/+uWs/IyNC9TXfPS+zs7FTUDh06pKiprWKYmZmpqHF1fZKNkdn829/+\nplrnytpEvjMym8Fy/vx5s1sgCrhAZ3Po0KGq9SNHjihqY8aMUdTUzmkTExMVtb///e86uiOSZIx5\nYwAAIABJREFUl9ZsxsfHe92WWo7+7//+T1EbN26cb01q4O68Wa3e09OjaZvh4e7fr/frnfylS5di\n9erVPNknkgyzSSQnZpNITswmkZyYTX10v5O/Y8cO2Gw2ZGVlweFwuH3X3Krq6urMboEsitn0rKam\nxuwWyKKYTVcOhwMOh8PsNoiYTSJJ+ZLNV155xfnxAw88gKSkpGC0GFS+HDd1T/Jramqwfft27Ny5\nE9evX8eVK1cwb948bN68We8mQ0p6ejpOnjxpdhtkQcymZ7m5uTh48KDZbZAFMZuu7HY77Ha783Vp\naal5zZClMZtEcvIlmz/96U9N6DC47jxu/uIXv3A7Vvfl+qtWrcL58+dx5swZVFZWIi8vj78MiSTA\nbBLJidkkkhOzSSQnZlM/vxbe00rr4gFq/Ln/wt3X9uvXT1F75JFHNNX+/Oc/K2p/+ctfdHRHJJ/V\nq1crar3lHqjp06crart37zahE6LQwkuXidxTO87k5+f7tU21465a7dNPP1XU3OX1Rz/6kaL2u9/9\nTkd3RHKKiIjwOkYtR2lpaYFoxy9GnHsbMsmfPHkyJk+ebMSmiMhAzCaRnJhNIjkxm0RyYjZ949fq\n+kREREREREQkD78m+W1tbZg9ezZSUlKQlpaGw4cPG9UXEfmB2SSSE7NJJCdmk0hOzKY+fl2uv2TJ\nEjz++OPYtm0burq6cO3aNaP6IiI/MJtEcmI2ieTEbBLJidnUR/ck//Lly9i3bx8qKipubqhPHwwe\nPFh1rGwLd/nTzxNPPKGoPfTQQ4paYmKi7n0Q+cOXbN6pq6srgJ0FVkJCgtktEHnkTzaJKHB8yaaW\nxaSDdd6rdYE+APjtb3+rqP3mN79R1IqKihS1t956S0d3RP7zJZuvvvqqy+u1a9dq2ods81Sj6L5c\n/+zZsxg+fDjmz5+PnJwcLFq0CNevXzeyNyLSgdkkkhOzSSQnZpNITsymfron+V1dXaitrcXixYtR\nW1uLgQMHoqyszMjeiEgHZpNITswmkZyYTSI5MZv66b5cf9SoUYiNjcX48eMBAIWFhSgvL1cdW1JS\n4vzYbrfDbrfr3a3U2tra0NbWBgBwOBzmNkOW5Us2raimpsbsFsiimE1XDoeDx0qSAs9pXbW0tKCl\npcXsNoh8yubBgwddvi4U+XLc1D3Jt9lsiI2NxenTp5GYmIjq6mqkpqaqjr39F2IoGzJkCIYMGQLg\n5i/+9957z+SOyIp8yaYV5ebmuhwIiIKF2XR15wSptLTUvGbI0nhO62rEiBEYMWKE8/VHH31kYjdk\nZb5k81vf+laQuws+X46bYUIIoXdHH374IZ555hl8/fXXSEhIwBtvvOGc5Dp3EBYGP3bRK7j7/sLD\nw0P+eyc5ac3mndRqu3fvVt1Hfn6+oqb28/78888rah0dHYragw8+qLqf2bNnK2ovvPCCpj6PHDmi\nqC1fvhxr1qxhNskUerMpI6MzZIXzBZIXz2lvUvv+eD5LZjLynFbLwpm9iaffSX49Qi8zM1P1JJqI\nzMVsEsmJ2SSSE7NJJCdmUx/dC+8RERERERERkVw4ySciIiIiIiIKEX5N8tetW4dx48YhIyMDc+bM\nQWdnp1F9EZEfmE0iOTGbRHJiNonkxGzqo3uSf+HCBbzyyiuora3FiRMn0NXVhcrKSiN7IyIdmE0i\nOTGbRHJiNonkxGzq59fCe93d3bh69SrCw8Nx7do1REdHG9VXr9JbVkIm69CbTbUVOqdNm2Z0ez75\n3ve+Z+r+iYwUSsfN2tpaRS0nJ8eEToj8F0rZ9AfPaUk2erMZHq58LzsjI0NRe/fddxW14cOH+96o\nZHS/kx8dHY1ly5YhLi4OMTExGDp0KKZOnWpkb0SkA7NJJCdmk0hOzCaRnJhN/XS/k9/a2oqqqirU\n19djyJAhKCwsxJYtW/D0008rxpaUlDg/ttvtsNvtencrNYfDAYfDYXYbZHG+ZNOKampqzG6BLIrZ\ndMVjJsmC57SumE2SBY+brnzJZphQuz5Xgz/96U9455138NprrwEAfv/73+Pw4cP49a9/7bqDsDDV\nS4CtwMrfO5nHl2xa0fLly7FmzRpmk4Iu1LL5wQcfKGr+XK7PYyaZhee0nln1+ybz+XPcjIiIUNRS\nU1MVtd58ub6nbOq+XD8uLg6HDh1CR0cHhBCorq5GSkqK7iaJyBjMJpGcmE0iOTGbRHJiNvXTfbn+\nAw88gMLCQmRnZ6Nv377Izs7GokWLjOyNiHRgNonkFGrZfOWVVxS1GTNmKGqzZs0KRjtEuoVaNolC\nhT/Z7O7uVtTq6uoUtXvvvVdzP2pXDOzfv19Re+ihhxQ1d++4q409dOiQ5p7c0X25vuYdWPgSHyt/\n7yS/3nJJsNF4uT7Jrrdk8wc/+IGi5s8kn8dMkp1Vf0at+n1T7xGs46aMk3zDL9cnIiIiIiIiIrl4\nneQvXLgQNpvN5bmCly5dQn5+PpKSkjBt2jS0tbUFtEkiUmI2ieTEbBLJidkkkhOzaTyvk/z58+fj\nnXfecamVlZVh6tSpOHXqFPLy8vDSSy8FrEEiUsdsEsmJ2SSSE7NJJCdm03ia7smvr6/HjBkzcOLE\nCQBAcnIy3nvvPdhsNjQ1NcFut+OTTz5R34HKfTxqu3T37OoxY8YoaiNHjvTWcsCo9b527VpFLTk5\nGd/5znd4DxMFlL/ZtCLek0/BYIVs2mw2RW369OmK2ogRIxS1Rx55RFGbMWMGc0kBZ/Q5rRVY9fum\n4LLCcTMQDL0nv6WlxXlwj4qKQktLi/7OiMgwzCaRnJhNIjkxm0RyYjb9Y8jCe1b+6wmRzJhNIjkx\nm0RyYjaJ5MRs+qaPni+y2Wxobm52Xj6hdine7UpKSpwf2+12TJ48Wc9upffZZ5/h73//OwDggw8+\nMLkbsiJfs2lF7m4NIgokZlOprq5O9ZnFRMHk7zmt3W4PbIMmcDgccDgcZrdBFsfjpn80TfKFEC7X\n+xcUFKCiogIrV67Epk2bMHPmTI9ff/svxFvbC0X33Xcf7rvvPgA37yPZunWryR1RqPM3m1aUm5uL\ngwcPmt0GhThm07v09HSkp6c7X/OYScFg9DltKLrzjxelpaXmNUOWweOmsbxO8p9++mk4HA589dVX\niIuLQ2lpKZ599lnMnj0bGzduRHx8PN566y2P27hzUq/210F3f83Pzc311qLpjhw5oqj17dvXhE7I\nSozIJhEZzyrZbG5uVtROnTql6WvffPNNo9sh8soq2STqbZhN43md5G/ZskW1vmfPHp935nA4NF3W\n9Omnn2Ls2LGGbc+XsYHYJlEgGJlNIjIOs+leU1MToqKizG6DLMqobPaW889A7JsoEHjcNJ4hC+9p\npfX+ns8++8zQ7fkyNhDbJCIiopuTfKLerrecfwZi30TUOwR1kk9EREREREREgcNJPhEREREREVGI\nCBMBXure6s80DNUnCVDvx2wymyQnK2eTuSSZMZtEcmI2lQI+ySciIiIiIiKi4ODl+kREREREREQh\ngpN8IiIiIiIiohDBST4RERERERFRiAjKJH/37t1ITk5GYmIiysvL3Y5raGhAXl4e0tLSkJ6ejg0b\nNnjcbk9PD3JyclBQUOB2TFtbG2bPno2UlBSkpaXh8OHDbseuW7cO48aNQ0ZGBubMmYPOzk7n5xYu\nXAibzYaMjAxn7dKlS8jPz0dSUhKmTZuGtrY2j/0SyUZLNn3NJRC8bDKXFKqYTSI5BSKbWnIJaM8m\nz2fJipjNO4gA6+7uFmPGjBHnzp0TnZ2dIjMzU3z88ceqYy9evCiOHTsmhBDiypUrIjEx0e1YIYRY\nu3atmDNnjpgxY4bbMcXFxWLjxo1CCCG+/vpr0dbWpjqusbFRjB49Wty4cUMIIcSTTz4pNm3a5Pz8\nvn37xLFjx0R6erqztmLFClFeXi6EEKKsrEysXLnSbR9EstGaTV9zKUTwsslcUihiNonkFKhsasml\nENqyyfNZsiJmUyng7+S///77GDt2LOLj49G3b1889dRTqKqqUh0bFRWFrKwsAEBkZCRSUlLQ2Nio\nOrahoQE7d+7EM88843bfly9fxr59+zB//nwAQJ8+fTB48GC347u7u3H16lV0dXXh2rVriI6Odn5u\n0qRJuPvuu13GV1VVobi4GABQXFyMt99+2+22iWSjNZu+5BIIbjaZSwpFzCaRnAKRTS25BHzLJs9n\nyWqYTaWAT/IbGxsRGxvrfD1q1CiPJyG3nDt3DsePH8fEiRNVP7906VKsXr3a43MRz549i+HDh2P+\n/PnIycnBokWLcP36ddWx0dHRWLZsGeLi4hATE4OhQ4di6tSpHntsaWmBzWYDcPOHpqWlxev3RSQL\nPdn0lkvA/Gwyl9TbMZtEcgpENrXkEtCeTZ7PkhUxm0pSLrzX3t6OwsJCrF+/HpGRkYrP79ixAzab\nDVlZWRBCQAihup2uri7U1tZi8eLFqK2txcCBA1FWVqY6trW1FVVVVaivr8eFCxfQ3t6OLVu2+NS3\ntx8Cot7MWy4BObPJXFKoYzaJ5GTU+SygPZs8nyXyzgrZDPgkPyYmBufPn3e+bmhoQExMjNvxXV1d\nKCwsxNy5czFz5kzVMTU1Ndi+fTsSEhJQVFSEvXv3Yt68eYpxo0aNQmxsLMaPHw8AKCwsRG1treo2\n9+zZg4SEBAwbNgwRERGYNWsWDhw44PF7s9lsaG5uBgA0NTVhxIgRHscTycSXbGrJJSBHNplL6u2Y\nTSI5GZ1NrbkEtGeT57NkRcymkl+TfC2rGE6YMAGfffYZ6uvr0dnZicrKSo8rFC5YsACpqalYsmSJ\n2zGrVq3C+fPncebMGVRWViIvLw+bN29WjLPZbIiNjcXp06cBANXV1UhNTVXdZlxcHA4dOoSOjg4I\nIVBdXY2UlBSXMXf+JaegoAAVFRUAgE2bNnk8wSIKJqOzqSWXgDnZZC6pN2E2KwAwmyQfM7KpNZeA\n9mzyfJZCDbNZAUBHNjUv0XcHX1bN37Vrl0hMTBT33XefeOmll9xuc//+/SI8PFxkZmaKrKwskZ2d\nLXbt2uWxD4fD4XHFw+PHj4vx48eLzMxM8cQTT4jW1la3Y0tKSkRycrJIT08X8+bNE52dnc7PFRUV\niZEjR4p+/fqJ2NhYsXHjRvGPf/xDTJkyRSQmJorHHntMXLp0yWOvRMFgdDb15FKI4GSTuaTehNlk\nNklOMmTTWy6F0J5Nns9SqGA29WczTAgPNxl4cOjQIZSWlmLXrl0AgLKyMoSFhWHlypUu46x+X4/O\nf14i3ZhNbZhNCjZm0zvmkszAbHrHbJIZmE3v3GWzj94Nqq1i+P777+vdXMgpKirC1q1bzW6DLMiX\nbL744ouorq7GlClTANy8j+hOY8eOdX5cUlKCkpISAMCJEycUY//4xz8CAPbt24eHH35Y8fW3xMfH\nOz/etGkTiouLkZeX5/H7un3f3ngba+WDAZnHn+NmZ2enota3b1/nx7d+5t0d7Nvb2wHcvATxueee\nAwC8+eabinH/+Z//6fy4ubkZNpsNdXV1mnok6q14TkskJytk09Mf0Pw5n5VydX0iIiIiIiIi8p3u\nd/J9XTXfavjOB5nFl2xWV1fjzJkzAIDRo0cHpT8zOBwOOBwOs9sgi+Nxk0hOzCaRnJhNV76cz+qe\n5N++iuHIkSNRWVnJy9Nvk56ejpMnT5rdBlmQL9mcMmUKRo8ejYSEBE3bttvtmsbFxcVpbReZmZmG\n7lttrN1ud6mVlpZq3haRUQJ53NSaj1u30WgxaNAgnd0Q9S48pyWSk9Wz6c/5rO6F94CbjzRYsmQJ\nenp6sHDhQjz77LPKHVj03tdb9+RzoRIyg9Zsnj171qV2+73y3ixYsEBR69NH+XfDyZMnK2pz5sxR\n7SdYwsLCmE0yhd7jZr9+/RS1W/fZ3+72+/Rvp/bzfuXKFUVtyJAhql+v1a3nBN9u0qRJilpOTo6i\nNm/ePOaSTMNzWs+YTTJLb8zm8OHDFbWWlhbVsf707ul8Vvc7+QAwffp0nDp1yp9NEFEAMJtEcmI2\nieTEbBLJidnUhwvvEREREREREYUITvKJiIiIiIiIQoTuSX5DQwPy8vKQlpaG9PR0bNiwwci+iEgn\nZpNITswmkZyYTSI5MZv66V54r6mpCU1NTcjKykJ7ezvuv/9+VFVVITk52XUHki2EECxceI/M4ks2\nX375ZZfamDFjFNu79957VfdTXV2tqH3rW99S1KZMmaKomf17gQvvkRn8OW6qLah39epVTePcUcuA\nWu2Pf/yj5m0+9dRTmsfeKTw8nLkkU/Cc1jtmk8zQG7KpdZG9QPTo6XxW9zv5UVFRyMrKAgBERkYi\nJSUFjY2NejdHRAZhNonkxGwSyYnZJJITs6mfIffknzt3DsePH8fEiRON2BwRGYTZJJITs0kkJ2aT\nSE7Mpm/8eoQecPM5vYWFhVi/fj0iIyON6Ckk1NXVmd0CWZyWbL7zzjvOj8eMGaN6uX4ocDgccDgc\nZrdBBIDHzVuYS5INs0kkJ2bzJl+Om7rvyQeArq4ufOc738G3v/1tLFmyRH0HFr1/iffkk5m0ZpP3\n5BMFl97jJu/JJwosntN6xmySWWTPZsjdkw8ACxYsQGpqqtt/cCIyB7NJJCdmk0hOzCaRnJhNfXS/\nk19TU4NHHnkE6enpCAsLQ1hYGFatWoXp06e77sCif/XkO/lkFl+yedddd7nU5s2bp9jez372M9X9\nqL1jmJCQoKjJ+DuA7+STGYw+bhYXFytqFRUVRrTqwpes+JN35pLMwnNa75hNMkNvyGZPT4+iFqx+\nPB03dd+Tn5ubi+7ubt1NEVFgMJtEcmI2ieTEbBLJidnUz5DV9YmIiIiIiIjIfH5P8nt6epCTk4OC\nggIj+iEigzCbRHJiNonkxGwSyYnZ9J3fk/z169cjNTXViF6IyEDMJpGcmE0iOTGbRHJiNn2n+558\nAGhoaMDOnTvx/PPPY+3atUb1RER+0prN69evu7yeOnWqYkxSUpLm/Vp5USIiLYw8bu7atcugrjxj\nrskKeE5LJCeZsjlw4EBFTdZjpF/v5C9duhSrV6+W9psjsipmk0hOzCaRnJhNIjkxm/rofid/x44d\nsNlsyMrKgsPh4KM17lBXV2d2C2RRzKaSw+GAw+Ewuw2yOGbTFXNJsmA2ieTEbLry5bgZJnT+az33\n3HN488030adPH1y/fh1XrlzBrFmzsHnzZtcdWPSvLkVFRdi6davlfxgp+PzJ5rZt2xS1f/qnf9K8\n796Sdz6Pm8xg9HFzxIgRilpzc7MhvZqBuSSz8JzWO2aTzCBbNtUu17969WpQ9q3G03FT9yT/du+9\n9x5efvllbN++XXXnVsRJPsnA12xykk8UHEYcNznJJzIez2nVMZtkNhmy2Zsm+X4tvNdb3XfffYra\n6dOnFTW1f7TnnntOUSsvLzemMaIgW7FihctrtQl9bzmpUcvriy++qKg9+OCDwWiHKOBkPOlW62np\n0qUmdEJEt7Pb7Yoab5ch8k1sbKzZLWhmyCR/8uTJmDx5shGbIiIDMZtEcmI2ieTEbBLJidn0jV+r\n6xMRERERERGRPDjJJyIiIiIiIgoRfk3y29raMHv2bKSkpCAtLQ2HDx82qi8i8gOzSSQnZpNITswm\nkZyYTX38uid/yZIlePzxx7Ft2zZ0dXXh2rVrRvVliHnz5qnWKyoqFDW1xcXUaqtWrdK078zMTGzd\nulXTWCKjac3m4sWLXV735kX2Dh06pKj9/Oc/V9SWL18ekJ6ItDDyuFlcXGxgZ4Fz8uRJRS0qKsqE\nTojck/2cVquenh7NY9WOpREREUa2Q+Q3mbJ5+fJl0/btK92T/MuXL2Pfvn3OCXOfPn0wePBgo/oi\nIp2YTSI5MZtEcmI2ieTEbOqn+3L9s2fPYvjw4Zg/fz5ycnKwaNEiXL9+3cjeiEgHZpNITswmkZyY\nTSI5MZv66Z7kd3V1oba2FosXL0ZtbS0GDhyIsrIyI3vrdc6fP4/9+/dj//79+NOf/mR2O2RRvmRz\n3bp1zv8OHjwY5E7NUVNTY3YLZFE8brpqbm7GiRMnnP8RmYXZJJITs+nK4XCgpKTE+Z8nui/XHzVq\nFGJjYzF+/HgAQGFhIcrLy/VuLiTExcUhLi4OwM178v/85z+b3BFZkS/ZXLp0aTBbk0Jubq5l/qBB\ncuFx05XNZoPNZnO+rqurM7EbsjJmk0hOzKYru90Ou93ufF1aWup2rO538m02G2JjY3H69GkAQHV1\nNVJTU/VujogMwmwSyYnZJJITs0kkJ2ZTP79W19+wYQPmzJmDr7/+GgkJCXjjjTeM6sspNzdXUWtu\nblbUXnzxRUVt9uzZqtv0ZwXx8HDl30Veeukl1bFPP/207v0Q+UNrNo8fP+7y+taVKL3Rm2++aXYL\nRF4Zedz85S9/aWBnvlNbmfvee+9V1MaNGxeMdoj8EoxzWn9oXTXfl3NctbFq+1E79yUKFtmzKSu/\nJvmZmZk4cuSIUb0QkUGYTSI5MZtEcmI2ieTEbOrDP80RERERERERhQi/Jvnr1q3DuHHjkJGRgTlz\n5qCzs9OovojID8wmkZyYTSI5MZtEcmI29dE9yb9w4QJeeeUV1NbW4sSJE+jq6kJlZaWRvRGRDswm\nkZyYTSI5MZtEcmI29fPrnvzu7m5cvXoV4eHhuHbtGqKjo3Vva+zYsW73cadVq1YpaoWFhYqaPwvs\n+SJY+yHSSms2//KXv7i8LigoCEZ7AfHJJ5+Y3QKRV3qPm2qLYQXr2KO2wB4AbNu2TdPYTz/9VFFT\nW1SXyExGntP6YuHChYraa6+9pqjxnJasyqxs9na638mPjo7GsmXLEBcXh5iYGAwdOhRTp041sjci\n0oHZJJITs0kkJ2aTSE7Mpn6638lvbW1FVVUV6uvrMWTIEBQWFmLLli2Wfmycw+GAw+Ewuw2yOF+y\n+cEHHzg/HjlyZDDbNE1NTY3ZLZBF8bjpqr6+HvX19Wa3QcRs3oHnsyQLZtOVL9nUPcnfs2cPEhIS\nMGzYMADArFmzcODAAcv+owOA3W6H3W53vi4tLTWvGbIsX7J5//33B7s90+Xm5uLgwYNmt0EWxOOm\nq/j4eMTHxztf79+/38RuyMqYTVc8nyVZMJuufMmm7sv14+LicOjQIXR0dEAIgerqaqSkpOjdHBEZ\nhNkkkhOzSSQnZpNITsymfrrfyX/ggQdQWFiI7Oxs9O3bF9nZ2Vi0aJHuRtQW5gGAnJwcRe2rr75S\n1LhQCNFNvmRz9+7dLq/z8/MVY/76178GpE+jvfvuu2a3QOSRL9n813/9V5fXveUY179/f0UtPFz5\nfsLJkyeD0Q6RJkaf0/rCzEX2iGRnZjbVnD171rR9+ypMuFs216gd+PmL6nvf+56idvtlCrf8+Mc/\n9ms/gRAWFuZ2VWIis4WFhSE2NtallpycrBgn4yRfLVdqEwk1y5cvx5o1a5hNklZYWJhikv/LX/7S\npG58W13/Zz/7maIWERGhqKn9AX/79u3MJUktEJNvM5+coRXPZ0l2wcpMR0eHoqb2x+1g8ZRN3Zfr\nExEREREREZFcOMknIiIiIiIiChFeJ/kLFy6EzWZDRkaGs3bp0iXk5+cjKSkJ06ZNQ1tbW0CbJCIl\nZpNITswmkZyYTSI5MZvG83pP/v79+xEZGYl58+bhxIkTAICVK1finnvuwYoVK1BeXo5Lly6hrKxM\nfQcBuEfi+9//vqL2+9//3vD9qFH75/rb3/6mqI0YMQKpqam8h4kCxohs/uAHP3Cp3drO7fbu3av6\n9YMHD/bvG7iDu6xcunRJUUtLS1PUmpqaNO2H9+RToBmRzTvv01U7lqr9DPvyGLqHH35Y81g1avcS\nP/LII4qaWu/jx49X1H71q18xlxRQMp7TButnXm0/r7/+uqL2wx/+UPPXExlFxmyqUVv8/dbj/bxx\nl6F/+Zd/UdTOnDmjqB0/flxRO3/+vP578idNmoS7777bpVZVVYXi4mIAQHFxMd5++21vmyEigzGb\nRHJiNonkxGwSyYnZNJ6ue/JbWlpgs9kAAFFRUWhpaTG0KSLSh9kkkhOzSSQnZpNITsymf/oYsRHZ\nHvVhlmPHjjkvpRg0aJDJ3RB5z+axY8ecH0dFRQW6HSnU1NSY3QKR12yWlJQ4P7bb7Xj00UcD3FHw\nff7552hoaDC7DSIXPKclkhOzefMRfmqP8VOja5Jvs9nQ3NwMm82GpqYmjBgxQs9mQk52djays7MB\n3Lwn/9VXXzW5I7IaX7N56+f1li+++CKQ7UkhNzcXBw8eNLsNshhfs3n7JD9UxcbGIjY21vn68OHD\nJnZDVsVzWiI5MZtKAwYMwIABA5yvL1++7Haspkm+EMLlpv6CggJUVFRg5cqV2LRpE2bOnOlHu777\nwx/+oKj5u/Ce2qIFX375paKm9QesqKjIr36ItPA3mxUVFV73obYgCAA89NBDitrtJ+y3NDc3K2o3\nbtxQ1O677z7V/ahNyLUuskdkFn+zqfcdC7VFgQJFrce+ffsqal1dXYra0aNHA9ITkTeyndOGhyvv\nnFVb1FIrd4twPffcc4qau0XMiMwgWzbV3HPPPYqa2rEwMjJSUXO3QJ/WRS07Ozs1jbvF6z35Tz/9\nNB566CGcPn0acXFxeOONN/Dss8/if//3f5GUlITq6mo8++yzPu2UiPzHbBLJidkkkhOzSSQnZtN4\nXt/J37Jli2p9z549hjfjKyGE5nc7HA4H7Ha7YeOIzGZGNi9evIiRI0d6Hffhhx8iMzNT0zY/+eQT\nJCcnex135swZJCQkaNomkZmMyqYvxyOtY0+ePIlx48YZuk1f+mxtbcXQoUM1jSUyGs9pieQkcza1\n8iXDHR0dLpfd+ztOja7V9Xsjh8Nh6DgiK7p48aKmcR9++KHmbZ46dUrTOLVnhhKFMl+OR1rHnjx5\n0vBt+tJnW1ub5rFEpI7ntES9m9bF87SOU2OZST4RERERERFRqDPkEXrezJo1CwDw0UeSM89QAAAg\nAElEQVQfITU11et4b+NuXQqhdXt69evXT1G79b142/+ECROwdevWgPVGZIRZs2Z5zVFOTo7z44sX\nLzpfx8fHK8YOHz4cwM1HSN5apFItR7cvxDV06FB885vfdH7tnRITEwHcfNzfrY9vz6EaT99Tenq6\nx68l6s283U4TGRmp6ZYbvR5++GFF7fZFxP72t7/hkUceUf3a/fv3B6wvIqNoOW7eTpZz2rS0NEVN\n6/n5n//854D1RWQUo7OpdZxahtUusb99Mb6jR49i/PjxADwvvHf7OLVFbDdu3Oi+L6F1ST+drP5M\nwwD/8xLpxmwymyQnK2eTuSSZMZtEcmI2lQI+ySciIiIiIiKi4OA9+UREREREREQhgpN8IiIiIiIi\nohARlEn+7t27kZycjMTERJSXl7sd19DQgLy8PKSlpSE9PR0bNmzwuN2enh7k5OSgoKDA7Zi2tjbM\nnj0bKSkpSEtLw+HDh92OXbduHcaNG4eMjAzMmTMHnZ2dzs8tXLgQNpsNGRkZztqlS5eQn5+PpKQk\nTJs2jY8Gol5HSzZ9zSUQvGwylxSqmE0iOQUim1pyCWjPJs9nyYqYzTuIAOvu7hZjxowR586dE52d\nnSIzM1N8/PHHqmMvXrwojh07JoQQ4sqVKyIxMdHtWCGEWLt2rZgzZ46YMWOG2zHFxcVi48aNQggh\nvv76a9HW1qY6rrGxUYwePVrcuHFDCCHEk08+KTZt2uT8/L59+8SxY8dEenq6s7ZixQpRXl4uhBCi\nrKxMrFy50m0fRLLRmk1fcylE8LLJXFIoYjaJ5BSobGrJpRDassnzWbIiZlMp4O/kv//++xg7dizi\n4+PRt29fPPXUU6iqqlIdGxUVhaysLAA3HzOQkpKCxsZG1bENDQ3YuXMnnnnmGbf7vnz5Mvbt24f5\n8+cDAPr06YPBgwe7Hd/d3Y2rV6+iq6sL165dQ3R0tPNzkyZNwt133+0yvqqqCsXFxQCA4uJivP32\n2263TSQbrdn0JZdAcLPJXFIoYjaJ5BSIbGrJJeBbNnk+S1bDbCoFfJLf2NiI2NhY5+tRo0Z5PAm5\n5dy5czh+/DgmTpyo+vmlS5di9erVHh+ZcPbsWQwfPhzz589HTk4OFi1ahOvXr6uOjY6OxrJlyxAX\nF4eYmBgMHToUU6dO9dhjS0sLbDYbgJs/NC0tLV6/LyJZ6Mmmt1wC5meTuaTejtkkklMgsqkll4D2\nbPJ8lqyI2VSScuG99vZ2FBYWYv369YiMjFR8fseOHbDZbMjKyoIQwu3zAbu6ulBbW4vFixejtrYW\nAwcORFlZmerY1tZWVFVVob6+HhcuXEB7ezu2bNniU99WfkYjhT5vuQTkzCZzSaGO2SSSk1Hns4D2\nbPJ8lsg7K2Qz4JP8mJgYnD9/3vm6oaEBMTExbsd3dXWhsLAQc+fOxcyZM1XH1NTUYPv27UhISEBR\nURH27t2LefPmKcaNGjUKsbGxGD9+PACgsLAQtbW1qtvcs2cPEhISMGzYMERERGDWrFk4cOCAx+/N\nZrOhubkZANDU1IQRI0Z4HE8kE1+yqSWXgBzZZC6pt2M2ieRkdDa15hLQnk2ez5IVMZtKAZ/kT5gw\nAZ999hnq6+vR2dmJyspKjysULliwAKmpqViyZInbMatWrcL58+dx5swZVFZWIi8vD5s3b1aMs9ls\niI2NxenTpwEA1dXVSE1NVd1mXFwcDh06hI6ODgghUF1djZSUFJcxd/4lp6CgABUVFQCATZs2eTzB\nIpKNL9nUkkvAnGwylxRqmE0iORmdTa25BLRnk+ezZEXMpgrNS/Sp2LVrl0hKShJjx44VZWVlHscl\nJiaK++67T7z00ktux+3fv1+Eh4eLzMxMkZWVJbKzs8WuXbs89uBwODyueHj8+HExfvx4kZmZKZ54\n4gnR2trqdmxJSYlITk4W6enpYt68eaKzs9P5uaKiIjFy5EjRr18/ERsbKzZu3Cj+8Y9/iClTpojE\nxETx2GOPiUuXLnnslShYjMymnlwKEZxsMpfU2zCbzCbJyexsesulENqzyfNZCiXMpr5shgnh4SYD\nD3p6epCYmIjq6mpER0djwoQJqKysRHJysss4q9/Xo/Ofl0g3ZlMbZpOCjdn0jrkkMzCb3jGbZAZm\n0zt32eyjd4O3P6oAgPNRBXf+owPAj370IwDA0aNHMX78ePz4xz9WjPnJT37i/Pj8+fOIi4sDcPOe\niN6oqKgIW7duNbsNsiBfsmlFy5cvx5o1a8xugyyI2SSSUyhlMykpyfnxl19+ieHDhwMATp06ZVZL\nRLr5kk0hBEpKSlBSUuJ2e7dPiG+NnTx5smLcvn37/G/eZLrvydf7aDwiCixmk0hOzCaRnJhNIjkx\nm/oF5RF6R48exdGjR3HhwgVcuHAhGLs0XV1dndktEJGK3np1EBERERGpKykpgcPhcP6v1em+XN+X\nRxXceqTAhQsXEB0d7XXbQ4YM0duWNNLT03Hy5Emz2yAL8vWxlVaTm5uLgwcPmt0GWRCzSSSnUM3m\nwIEDzW6ByC++ZPPW5N5ut2vattZxvZXuSf7tjyoYOXIkKisr3d6D/tprr7m8/s1vfqN3t2598MEH\nilp2drbqWLUFCv76178qat/+9rf9b4woyHzJJhEFD7NJJKfems3p06crajt37tT89Wrnwz/96U8V\ntVdffdW3xogM4ks2hRCYPHmy8+c6PDwoF6yjs7NTUevXr19Q9u2J7kl+REQEfv3rXyM/Px89PT1Y\nuHCh4ll/RBR8zCaRnJhNIjkxm0RyYjb10z3JB27+BZGrdRLJh9kkkhOzSSQnZpNITsymPsG5joGI\niIiIiIiIAk73JL+hoQF5eXlIS0tDeno6NmzYYGRfRKQTs0kkJ2aTSE7MJpGcmE39dF+u36dPH6xd\nuxZZWVlob2/H/fffj/z8fCQnJxvZHxH5iNkkkhOzSSQnZpNITsymfron+VFRUYiKigIAREZGIiUl\nBY2Njar/6D09Pfo7VFFfX6+oxcbGKmphYWGqX69WnzZtmqLW3d2tqD355JOK2n//93+r7ofIDL5k\nk4iCh9kkkpNs2UxKSlLURo8eraitWrVKUXN37qvVokWLFDWurk9m8SWbwVhNX20eGBEREfD96mHI\nv8a5c+dw/PhxTJw40YjNEZFBmE0iOTGbRHJiNonkxGz6xq/V9QGgvb0dhYWFWL9+PSIjI43oKSTU\n1dWZ3QJZHLOprqamxuwWyOKYTSI5MZs3HTlyBEePHjW7DSInZtN3fk3yu7q6UFhYiLlz52LmzJlG\n9RQS0tPTcfLkSbPbIItiNt3Lzc3FwYMHzW6DLIrZJJITs/n/TJgwARMmTHC+/s1vfmNiN2R1zKY+\nfl2uv2DBAqSmpmLJkiVG9UNEBmA2ieTEbBLJidkkkhOzqU+YEELo+cKamho88sgjSE9PR1hYGMLC\nwrBq1SpMnz7ddQcaFwBZu3atav2RRx5R1HJychQ1fxca0UptEcH/+I//UNQSExMxffp06PznJdLN\n6GwGglqO3GUlNTVVUTt16pTufS9fvhxr1qxhNinoQi2bgVhsiLkkM/SGbM6dO1dR++EPf6ioPfzw\nw37tRy2D4eHhzCaZojdk02zusqn7cv3c3FzV1eeJyFzMJpGcmE0iOTGbRHJiNvUL/LMGiIiIiIiI\niCgoOMknIiIiIiIiChF+T/J7enqQk5ODgoICI/ohIoMwm0RyYjaJ5MRsEsmJ2fSdX4/QA4D169cj\nNTUVly9fdjtm6dKlLq9ffvllv/bZ3t6uqH3jG9/Q/PVqCxSsW7dOUVu+fLmi9vHHHytqw4YNU9QG\nDRqkuR+iQNCSTaOpZfuxxx5T1NQWSHG3aMpHH32kqAViwS+iYDEjm2oL6qnxJZtq90kym9Sbaclm\nUlKSy2t/FoJV2x4ADBgwQFELxMJiaufDr7/+uuH7IfKXGcdN2agdx8PD3b9f79c7+Q0NDdi5cyee\neeYZfzZDRAZjNonkxGwSyYnZJJITs6mPX5P8pUuXYvXq1ZZ+bAGRjJhNIjkxm0RyYjaJ5MRs6qP7\ncv0dO3bAZrMhKysLDofD4/MzDx486Px41KhRencpvY8++sh5Of/QoUNN7oasypdsWlFNTY3ZLZBF\nMZtEcvIlm19++aXz44EDBwajvaA7deqU37chEBmBx01XDocDDodD01jdk/yamhps374dO3fuxPXr\n13HlyhXMmzcPmzdvVoz91re+pXc3vUpqaipSU1MBAPHx8XjjjTdM7oisyJdsWlFubq7LHx6JgoXZ\nJJKTL9kcPny4CR0GV1JSkstaAf/zP/9jYjdkZTxuurLb7bDb7c7Xv/jFL9yODRMG/Enkvffew8sv\nv4zt27crdxAWplgowN/LLdRa9nebaosZ+LOAUFFREbZu3Wr5vziRubxlU4u7775btb5t2zZFLS8v\nT9M2fcmrWoY8LTTizfLly7FmzRpmk0xlRDbVuFtgL1iLdvmTTXfbJAomb9l8/PHHXWo7d+7UtF21\nBfZubfNO/fv3V9QyMzMVNbV7lB9++GHV/fibV2aTzBao46aM1BadTklJUdTCwsLcZtPvR+gRERER\nERERkRz8foQeAEyePBmTJ082YlNEZCBmk0hOzCaRnJhNIjkxm77hO/lEREREREREIcKvSX5bWxtm\nz56NlJQUpKWl4fDhw0b1RUR+YDaJ5MRsEsmJ2SSSE7Opj1+X6y9ZsgSPP/44tm3bhq6uLly7ds2o\nvojID8wmkZyYTSI5MZtEcmI29dE9yb98+TL27duHioqKmxvq0weDBw9WHWv0ioeBWEFRbYXR7u5u\nRc2fFfeJgsGXbGphs9lU62or6Yfa6qZERjI6m2qYQSLf+ZLNL774Qtc+fMnmjRs3FLWOjg5FbdKk\nSbp6IeotgnHclFFVVZWipra6vie6L9c/e/Yshg8fjvnz5yMnJweLFi3C9evX9W6OiAzCbBLJidkk\nkhOzSSQnZlM/3ZP8rq4u1NbWYvHixaitrcXAgQNRVlamOrakpMT5n8Ph0LvLXqWurs7sFsiifMmm\nFdXU1JjdAlkUs0kkJ1+y2djY6Pzv8uXLQe6UyFp43HTlcDhc5tWe6L5cf9SoUYiNjcX48eMBAIWF\nhSgvL1cd662JUJSeno6TJ0+a3QZZkC/ZtKLc3FwcPHjQ7DbIgphNIjn5ks2YmJhgtkZkaTxuurLb\n7bDb7c7XpaWlbsfqfiffZrMhNjYWp0+fBgBUV1cjNTVV7+aIyCDMJpGcmE0iOTGbRHJiNvXza3X9\nDRs2YM6cOfj666+RkJCAN954w6i+pKC2GF9PT4/mr9+6dauR7RBpZmQ209PTVetGL/AlhFCtP/jg\ng4buh8hMgT5uPvTQQ6r1AwcO6N6mu2yqHSOJeiut2Txy5IjXbSUlJRndHpqbmxU1tXuTBw4caPi+\nicwU6vNNNbm5uX5vw69JfmZmpqZfdkQUXMwmkZyYTSI5MZtEcmI29eGf4YmIiIiIiIhChF+T/HXr\n1mHcuHHIyMjAnDlz0NnZaVRfROQHZpNITswmkZyYTSI5MZv66J7kX7hwAa+88gpqa2tx4sQJdHV1\nobKy0sjeiEgHZpNITswmkZyYTSI5MZv6+XVPfnd3N65evYrw8HBcu3YN0dHRRvUlLaMXGyMKBL3Z\nHDx4sKL2z//8z5r3q7ZA19mzZxW1M2fOKGrLly9X3WZXV5eiFhERoah1d3draZHIVIE+bh4+fFi1\nrrZInlpmfve73ylq9fX1/jdGJDkjs+nvuaLasfS9995T1P7whz8oaj/84Q8196T2OyAhIUFR4+8A\nMpMV55uTJ09W1HxZ/B3w45386OhoLFu2DHFxcYiJicHQoUMxdepUvZsjIoMwm0RyYjaJ5MRsEsmJ\n2dRP9yS/tbUVVVVVqK+vx4ULF9De3o4tW7YY2RsR6cBsEsmJ2SSSE7NJJCdmUz/dl+vv2bMHCQkJ\nGDZsGABg1qxZOHDgAJ5++mnF2JKSEufHdrsddrtd726l5nA44HA4zG6DLM6XbFpRTU2N2S2QRTGb\nRHJiNl11dHSgo6PD7DaImM07+DLX1D3Jj4uLw6FDh9DR0YH+/fujuroaEyZMUB17+yQ/lN35B4zS\n0lLzmiHL8iWbVpSbm4uDBw+a3QZZELNJJCdm09WAAQMwYMAA5+u2tjYTuyErYzZd+TLX1D3Jf+CB\nB1BYWIjs7Gz07dsX2dnZWLRokd7NEZFBfMlmVlaWy+tXX31VMebBBx9U/Vq1hYG2bdumqP31r39V\n1I4cOaKonThxQnU/Y8aMUdSmTJmiqDU3Nytqn376qaLWt29f1f0QBVowjpu+LMwzfvx4Re3ChQuK\nWlNTk189EckuGNnMzMxUrastJPvBBx9o2ua6desUNXcL76lRW5BTbbFctXFEwWDV+abaOfbjjz/u\n0zb8Wl3/hRdewAsvvODPJogoAJhNIjkxm0RyYjaJ5MRs6sM/zRERERERERGFCE7yiYiIiIiIiEKE\n10n+woULYbPZkJGR4axdunQJ+fn5SEpKwrRp07ggB5EJmE0iOTGbRHJiNonkxGwaL0yo3dl/m/37\n9yMyMhLz5s1zLoy1cuVK3HPPPVixYgXKy8tx6dIllJWVqe8gLEx18QArsPL3ToFnRDa///3vu9Q2\nb96sOk6N2s/2v/3bvylqO3bsUNROnjypuk2tJk+erKh94xvfUNQ++ugjRe2ZZ57Bc889x2xSwBiR\nTatiLimQjMhmUlKS1/2oZfjOhW5viY+PV9TUFt77/PPPNe1n0KBBqvs5evSoal0Lns9SoPG4qZ+7\nbHp9J3/SpEm4++67XWpVVVUoLi4GABQXF+Ptt982oEUi8gWzSSQnZpNITswmkZyYTePpuie/paUF\nNpsNABAVFYWWlhZDmyIifZhNIjkxm0RyYjaJ5MRs+sevR+jd4u0SiZKSEufHdrsddrvdiN1Kx+Fw\nwOFwmN0GkZO3bH744YfOj2/9Ig1F169fx/Xr1wEAe/bsMbkbImtfWkgkM2/Z/PLLL50fDxw4EAMH\nDgx0S0HH81mSEY+bvtE1ybfZbGhubobNZkNTUxNGjBjhcfztk/xQducfMEpLS81rhizJ12xmZmYG\nqTNz3XXXXbjrrrsAAFOnTsW7775rckdkNb5mk4iCw9dsDh8+PEidmYfnsyQDHjf9o+lyfSGEy039\nBQUFqKioAABs2rQJM2fODEhzROQZs0kkJ2aTSE7MJpGcmE1jeV1d/+mnn4bD4cBXX30Fm82G0tJS\nfPe738Xs2bPx+eefIz4+Hm+99RaGDh2qvgMLr8hp5e+dAs+IbP7+9793qd252r4naj/bs2fPVtQO\nHDigqF28eFHzfrRKTk5W1IYNG6aoFRUV4ac//SmzSQFjRDZ7A7XLlK9du+bXNplLCiQjsql2rLnT\n2LFjFbUpU6aojr11ldnt6urqFLW9e/cqal1dXYra6dOnVffT09OjWteC57MUaFY5bgaCu2x6vVx/\ny5YtqnU997U6HA5N9+MbPc7sbRIFglHZ/Pjjj5GSkqJprNaf+S+++AL33nuvT30Y6erVq24fI0QU\naEYeN3uD7u5uREREmN0GkVdGZdOXY8yXX36p6RL/U6dOaXo8ny/7F0JomvzwfJbMZrXjZjDoWl1f\nL62LeBg9zuxtEsns448/1jxW68/8F198obMbY/j7biIRadfd3W12C0RB5csx5quvvtI0zt078Gqu\nXr2qeawWPJ8lCj1BneQTERERERERUeAY8gg9Iuqd+vfvj4iICPTv39+Q7Q0ePNi53Vsf33PPPYpx\nt99HqPWyQ2/jbr//vr29HcOGDVO9d0vt/kci2dx7770+XRJsVI58GXsrS62trc6s3XpUpZ5tmn0F\nEJEWw4YNcx5j3BkyZIjz4/79+2PIkCFujz0DBgwAAPTp08f5cWRkpOp+b7ly5QqGDRumek/+7bfK\ntbe3q26LKBSZfdw0Y9+ejpteF97zl5UXQgC4iBDJi9lkNklOVs4mc0kyYzaJ5MRsKgV8kk9ERERE\nREREwcF78omIiIiIiIhCBCf5RERERERERCGCk3wiIiIiIiKiEBGUSf7u3buRnJyMxMRElJeXux3X\n0NCAvLw8pKWlIT09HRs2bPC43Z6eHuTk5KCgoMDtmLa2NsyePRspKSlIS0vD4cOH3Y5dt24dxo0b\nh4yMDMyZMwednZ3Ozy1cuBA2mw0ZGRnO2qVLl5Cfn4+kpCRMmzYNbW1tHvslko2WbPqaSyB42WQu\nKVQxm0RyCkQ2teQS0J5Nns+SFTGbdxAB1t3dLcaMGSPOnTsnOjs7RWZmpvj4449Vx168eFEcO3ZM\nCCHElStXRGJiotuxQgixdu1aMWfOHDFjxgy3Y4qLi8XGjRuFEEJ8/fXXoq2tTXVcY2OjGD16tLhx\n44YQQognn3xSbNq0yfn5ffv2iWPHjon09HRnbcWKFaK8vFwIIURZWZlYuXKl2z6IZKM1m77mUojg\nZZO5pFDEbBLJKVDZ1JJLIbRlk+ezZEXMplLA38l///33MXbsWMTHx6Nv37546qmnUFVVpTo2KioK\nWVlZAG4+IzQlJQWNjY2qYxsaGrBz504888wzbvd9+fJl7Nu3D/Pnzwdw8xmkt57draa7uxtXr15F\nV1cXrl27hujoaOfnJk2ahLvvvttlfFVVFYqLiwEAxcXFePvtt91um0g2WrPpSy6B4GaTuaRQxGwS\nySkQ2dSSS8C3bPJ8lqyG2VQK+CS/sbERsbGxztejRo3yeBJyy7lz53D8+HFMnDhR9fNLly7F6tWr\nPT4X8ezZsxg+fDjmz5+PnJwcLFq0CNevX1cdGx0djWXLliEuLg4xMTEYOnQopk6d6rHHlpYW2Gw2\nADd/aFpaWrx+X0Sy0JNNb7kEzM8mc0m9HbNJJKdAZFNLLgHt2eT5LFkRs6kk5cJ77e3tKCwsxPr1\n6xEZGan4/I4dO2Cz2ZCVlQUhBIQQqtvp6upCbW0tFi9ejNraWgwcOBBlZWWqY1tbW1FVVYX6+npc\nuHAB7e3t2LJli099e/shIOrNvOUSkDObzCWFOmaTSE5Gnc8C2rPJ81ki76yQzYBP8mNiYnD+/Hnn\n64aGBsTExLgd39XVhcLCQsydOxczZ85UHVNTU4Pt27cjISEBRUVF2Lt3L+bNm6cYN2rUKMTGxmL8\n+PEAgMLCQtTW1qpuc8+ePUhISMCwYcMQERGBWbNm4cCBAx6/N5vNhubmZgBAU1MTRowY4XE8kUx8\nyaaWXAJyZJO5pN6O2SSSk9HZ1JpLQHs2eT5LVsRsKvk1ydeyiuGECRPw2Wefob6+Hp2dnaisrPS4\nQuGCBQuQmpqKJUuWuB2zatUqnD9/HmfOnEFlZSXy8vKwefNmxTibzYbY2FicPn0aAFBdXY3U1FTV\nbcbFxeHQoUPo6OiAEALV1dVISUlxGXPnX3IKCgpQUVEBANi0aZPHEyyiYDI6m1pyCZiTTeaSehNm\nswIAs0nyMSObWnMJaM8mz2cp1DCbFQB8z2aY8HT9gQc9PT1ITExEdXU1oqOjMWHCBFRWViI5Odl1\nBxa/5EfnPy+RbsymNswm/X/s3X14FfWd//8XBLxhURApB4FACbdJSAgYxRVWziKCqytUGqyRAkW4\nWK+lXmjxK921q8F2MRSFQvX77fb6roK7AsW9MWwBe0nqsRAEagMKVkDuDQi0CuFeSDK/P/xxvoSZ\nk8yZM+fMJznPx3XlupJ3PmfmDVdemfnkzHwm1chm48glgkA2G0c2EQSy2bhY2fT8Tn48q+ano+Li\n4qBbQJoimw176qmngm4BaSqebFqWpeeeey76V/3GPtyODWKbdXV10Y9nn31WdXV1Ki4utn0AQfE7\nm04/87NmzXL8ePzxx/X444/r9ttvj36eiPbt20c/rr322ujn3/nOd2wfTrKysmwfQFCa0nHTz31f\n/fujrq5Or732mu2jIZ4n+V5XzQeQXGQTMBPZBMxENgEzkU3vWgXdQHO1ffv2oFsA4KCioiLoFoBG\nlZSUKBKJqKSkROFwWOFwOOiWfHfs2DEe1YUmJx2yef78+ZiPzgRMlQ7Z3Llzp3bu3OlqrOdJfryr\n5qebvLw87dixI+g2kIbIZsOGDh2q999/P+g2kIbiyeblkxW3Jyl+j0vFNkOhUPT5v5I4ZiIwJmQz\nGcfpVq28neZff/31uv7666Nfnzx50q+WgLiYkE2Tjq/9+/evtx5BQ7fjer5cP95V8wGkBtkEzBRv\nNk2akKd6m0AqmZDNbt26ud6mW61bt/Z9m0AqmZDNpnJ8vZrnd/IzMjL08ssva9SoUaqrq9PUqVNt\njwFIll/+8pe2WseOHW21WL8wnVYh/OlPf2qr/ed//qeH7oBgBZlNALGlazadVj1+4403bLXly5en\noh3Axu9sOv3Mz58/33Hs5WdgX8kpC3/+859d7btz586O9aNHj9pqV97rfNkdd9xhq+3bt8/VvgG/\nxZPNq+d3TreDXXn1mMmcfodMnjzZVpsyZUrMbSR0T/69996rXbt2JbIJAElANgEzkU3ATGQTMBPZ\n9Mbz5foAAAAAAMAsnif5VVVVGjFihHJzc5WXl6fFixf72RcAj8gmYCayCZiJbAJmIpveeb5cv1Wr\nVlqwYIEKCgp05swZ3XrrrRo1alS9Ff8ApB7ZBMxENgEzkU3ATGTTO8+T/M6dO0cX92jbtq2ys7N1\n+PBh3//Tt2zZYqsVFhb6ug9JWrlypa2WkZHh+36AZEtVNgHEh2z+P06LCgFBSUU2Y/3MOy0E5rQg\nntNj7Hr06GGrxVp02m3mNmzY4GockArxZPO//uu/6n09evTolPSYDE6LxP/3f/93XNvw5Z78AwcO\naNu2bRoyZIgfmwPgE7IJmIlsAmYim4CZyGZ8Ep7knzlzRkVFRVq0aJHatm3rR08AfEA2ATORTcBM\nZBMwE9mMX0KP0KupqVFRUZEmTpyosWPH+tVTs7B9+/agW0AaI5uxVVRUBN0C0ueQjJQAACAASURB\nVJjbbJaUlEQ/D4fDCofDyW8uxSKRiCKRSNBtAJLI5pUuXLigCxcuBN0GIMl9Nn/1q19FP8/NzW3S\nl+vHsmPHDn388ceuxrawnC76d2nSpEnq2LGjFixYEHsHCd53l6p78p3+GxK5J7+4uFjLly933C6Q\nbKnIZlP11FNP6cUXXySbCITbbKbjz2e6/rthhqCy6bS9W2+91VZzevPI6Z78nj17Ou7H6Zi/c+dO\nV+MOHTpENhEYt9n8j//4j3o1p0l+U7kKwO09+d/+9rdjZtPzO/kVFRV64403lJeXp0GDBqlFixaa\nO3eu7r33Xq+bdOQ0oU/G5MRpm7W1tbYai/HBdKnKJoD4kE3ATEFm0+n884MPPrDViouLbbXDhw/b\nauXl5Y77ycrKstU+++wzW61Xr16OrweCEE82q6qq6n195swZ25imMsl3+r3w4IMPxrUNz5P8oUOH\nOk6CAQSLbAJmIpuAmcgmYCay6Z0vq+sDAAAAAIDgMckHAAAAAKCZSHiSX1dXp8GDB2vMmDF+9APA\nJ2QTMBPZBMxENgEzkc34JfQIPUlatGiRcnJydOrUKT/6sQlyBXCnff/5z3+21Tp27JiKdoC4JDub\nbjk9IePkyZO2Wn5+vuPrv/GNb9hqO3bssNUGDhzooTsg9UzJZqKcVvQ9fvy4rRYKhVLRDpAwU7LZ\nsqX9Pbjly5fbag899JCtFmul7b1797ra95EjR1yNA1LJTTavnqOdOHHCNqZz586+95Yop8w6PcLy\n+uuvj2u7Cb2TX1VVpTVr1mjatGmJbAaAz8gmYCayCZiJbAJmIpveJDTJf/LJJzV//vy0fd42YCqy\nCZiJbAJmIpuAmcimN54v11+9erVCoZAKCgoUiURiXh6UrrZv3x50C0hTZLNhFRUVQbeANBVPNktK\nSqKfh8NhhcPh5DeYYpFIRJFIJOg2ALJ5ldraWtXV1QXdBhBXNt97773o5z169EhFeykXz3HT8yS/\noqJCq1at0po1a3T+/HmdPn1akyZN0uuvv+51k81KXl6e433DQLKRzYYNHTpU77//ftBtIA3Fk80r\nJxLN1dUTpDlz5gTXDNIa2awvIyNDGRkZ0a9ramoC7AbpLJ5sDh8+PIAOUyue46bny/Xnzp2rQ4cO\nad++fVqxYoVGjBjBJAIwANkEzEQ2ATORTcBMZNO7hFfXT7b//u//ttUefPDBlOzb6d6PDh062Gr9\n+vWz1W655Zak9ASYbM2aNbZaYWFhQtt0ymFeXp6tVltba6td+U4EAH85ZbNTp04BdAIEx+nyYafV\n8WPdT+w0Yfnud7/rapsrV6601RI97nGZPpqqq58g4ZSP5557LlXtuOb0u+G6666z1Zye8NYQXyb5\nw4cPT4tLJICmhmwCZiKbgJnIJmAmshmfhFbXBwAAAAAA5khokl9dXa3x48crOztbubm52rx5s199\nAUgA2QTMRDYBM5FNwExk05uELtefOXOm7rvvPr355puqqanRuXPn/OoLQALIJmAmsgmYiWwCZiKb\n3rSwPD5E+9SpUxo0aJBtkQPbDmIsNOLWNddcY6t99dVXCW0zEU7/XT/+8Y9ttQEDBujb3/42zyhH\nyqUqm06cFuxJxn6cOGXtZz/7ma126623avjw4WQTKRdPNtPx5zNd/90IXjzZvPo457QgXjycjpGJ\nLH4X67VuF+RzOm4+8cQTZBOBiCebVy9W57Qw+rZt23ztL1mc8lZaWmqr/eM//mPMbHr+zbR//351\n7NhRU6ZM0eDBgzV9+nSdP3/e6+YA+IRsAmYim4CZyCZgJrLpnefL9WtqalRZWalXXnlFhYWFeuKJ\nJ1RaWqo5c+b42V+Tsn//fh04cECS9PHHHwfbDNIW2bTbs2eP9uzZI0n66KOPAu4G6SqebJaUlEQ/\nD4fDCofDqWs0RSKRiCKRSNBtAAllszn69NNPo8dMIEjxZPPSpUvRzxO9wsZU+/bt0759+1yN9TzJ\n79atmzIzM6PPwC4qKtK8efO8bq5Z6Nmzp3r27Cnp68v1nZ7PCCQb2bTr3bu3evfuLenry/WXLFkS\nbENIS/Fk88qJRHN19R8v0vkPkQgW2ayvT58+6tOnT/Tr3/zmNwF2g3QWTzZbt26dytYCkZWVpays\nrOjXv/3tb2OO9fxnjlAopMzMTO3evVuSVF5erpycHK+bA+ATsgmYiWwCZiKbgJnIpneeF96TpA8/\n/FDTpk3TpUuXlJWVpddee03t2rWrv4MEF93ye0GSZIi54EHLlixUgkCkIptOTPt5J5swjdtsPvTQ\nQ/Vqv/rVr1LSn1Mu3nzzTcexV/eYKBbeQ5DcZvPqc9BQKGTb1p/+9KeEennppZdstR/84AeuXhsr\nQ7/73e9sNacM33PPPbbaG2+8QTYRGD/PaZ1y5JS3VHLK1n333Wervf32265fLyX4CL2BAwfq97//\nfSKbAJAEZBMwE9kEzEQ2ATORTW+a56oEAAAAAACkISb5AAAAAAA0EwlN8hcuXKgBAwYoPz9fEyZM\n0MWLF/3qC0ACyCZgJrIJmIlsAmYim954XnjvyJEjGjZsmHbu3KlrrrlG3/nOd3T//fdr0qRJ9XeQ\nhMW9nBYjWL16te/7SRSLCCEIQWZz4cKFttoTTzzh+34SRTYRhHiy+fDDD9erZWRk2LY3YMAAW+0P\nf/iD477feustW62mpsZWc/q9UFRU5LhNvx8TSy4RlHiyefXCdO+8805C+/7Wt77lqrZq1Spb7T/+\n4z9stVjH3MWLF7vqp6CgwFbbtm0b2UQgUnFO6/T41meffdbz9iTnxfAmTJjgOHb58uW+70tKcOG9\n2tpanT17Vi1bttS5c+fUpUuXRDYHwCdkEzAT2QTMRDYBM5FNbzxfrt+lSxfNmjVL3bt3V9euXdW+\nfXuNHDnSz94AeEA2ATORTcBMZBMwE9n0zvM7+SdPnlRZWZkOHjyodu3aqaioSMuWLdMjjzziZ39N\nSiQSUSQSCboNpDmyaUc2YYJ4srl9+/bo5506dWqW71yQS5ginmzu3bs3+vlNN92UyjZT5vTp0zpz\n5kzQbQCc0ybA8yR/3bp1ysrKUocOHSRJ48aN08aNG9P6Pz0cDiscDke/drrHA0g2smlHNmGCeLKZ\nl5eX6vZSjlzCFPFks1evXqluL+VuuOEG3XDDDdGvjx07FmA3SGec03rneZLfvXt3bdq0SRcuXNC1\n116r8vJy3XbbbX72FtOaNWtstRkzZthqr7zySiraAYwSZDZ/8IMf2GqPPfaYrXbdddeloh3AKPFk\nc8WKFSnu7mtOC/g4Le4lSRs2bLDVhg4daqvV1dXZaidOnPDQHZAc8WQz0YX2rrZz505bbfPmzbZa\ndna2rdaypf9Pwr7yKiIgaKk4p33uuedc1Zoaz78dbr/9dhUVFWnQoEEaOHCgLMvS9OnT/ewNgAdk\nEzAT2QTMRDYBM5FN7zw/Qs/1DpLwmC4nf//3f2+rBf1OPo8DgsmSkU2nbZ47d85WC/qdfLIJk6Xq\nuOlWrH5+97vf2WqJvJP/jW98g1zCaMnIZv/+/W21v/7rv7bVLl+ufKV//ud/9r0fp8d11tbWkk0Y\nzbTjZirFyqb/1/kAAAAAAIBANDrJnzp1qkKhkPLz86O1EydOaNSoUerXr59Gjx6t6urqpDYJwI5s\nAmYim4CZyCZgJrLpv0Yn+VOmTNFvfvOberXS0lKNHDlSu3bt0ogRI/TCCy8krUEAzsgmYCayCZiJ\nbAJmIpv+c3VP/sGDB/XAAw/oo48+kvT1/UPvvfeeQqGQjh49qnA47Lg6qBTsPRKx9v3ggw/aav/5\nn/+ZlP1zDxOSqSlks0+fPrba7t27U7LvWMgmkq0pZDMZnHqvra119dqWLVuSSyRdumbTrWeeecZW\n++d//meyiaQjm974ek/+8ePHFQqFJEmdO3fW8ePHvXcGwDdkEzAT2QTMRDYBM5HNxLTyYyPp/NeT\nK0UiEUUikaDbAKLI5tfIJkxDNsklzEQ2v35H9eDBg0G3AdRDNuPjaZIfCoV07Nix6OUTnTp18ruv\nJikcDiscDke/njNnTnDNIC2RTWdkE0Ejm3ZX5/L5558PrhmkLbJp16NHD/Xo0SP69fr16wPsBumK\nbCbG1eX6lmXVu95/zJgxWrJkiSRp6dKlGjt2bFKaA9AwsgmYiWwCZiKbgJnIpr8aXXjvkUceUSQS\n0RdffKFQKKQ5c+boW9/6lsaPH6/PPvtMPXr00MqVK9W+fXvnHTSRSyuc+jx79qytdv3118e1TRYq\nQbI0lWw67Wfv3r22Ws+ePVPRjiSyieRqKtlMlQ4dOthqX375peNYcolkIpuNe/XVV221Rx99lGwi\nqcimd7Gy6Wp1/UQ0lf/0y31alhX9vLFJfiQSqXepodM2+aUIU5kyyd+0aZPuuOOORif5jeUtnrFk\nEyZrKsdNt66c5F+6dEmtW7dmko8mqbll0wmTfDRF6ZDNWHxdXR9fY8EgIHGbNm1yNS6evJFNwEyX\nLl0KugUAAJo9JvkAAAAAADQTTPIBAAAAAGgmuCc/ybiHCaYim2QTZkrnbJJLmIxsAmYim3ZJn+QD\nAAAAAIDU4HJ9AAAAAACaCSb5AAAAAAA0E0zyAQAAAABoJlIyyX/77bfVv39/9e3bV/PmzYs5rqqq\nSiNGjFBubq7y8vK0ePHiBrdbV1enwYMHa8yYMTHHVFdXa/z48crOzlZubq42b94cc+zChQs1YMAA\n5efna8KECbp48WL0e1OnTlUoFFJ+fn60duLECY0aNUr9+vXT6NGjVV1d3WC/gGncZDPeXEqpyya5\nRHNFNgEzJSObbnIpuc8m57NIR2TzKlaS1dbWWr169bIOHDhgXbx40Ro4cKD1ySefOI79/PPPra1b\nt1qWZVmnT5+2+vbtG3OsZVnWggULrAkTJlgPPPBAzDGTJ0+2Xn31VcuyLOvSpUtWdXW147jDhw9b\nPXv2tL766ivLsizroYcespYuXRr9/vr1662tW7daeXl50drTTz9tzZs3z7IsyyotLbVmz54dsw/A\nNG6zGW8uLSt12SSXaI7IJmCmZGXTTS4ty102OZ9FOiKbdkl/J3/Lli3q06ePevToodatW+vhhx9W\nWVmZ49jOnTuroKBAktS2bVtlZ2fr8OHDjmOrqqq0Zs0aTZs2Lea+T506pfXr12vKlCmSpFatWunG\nG2+MOb62tlZnz55VTU2Nzp07py5dukS/N2zYMN100031xpeVlWny5MmSpMmTJ+utt96KuW3ANG6z\nGU8updRmk1yiOSKbgJmSkU03uZTiyybns0g3ZNMu6ZP8w4cPKzMzM/p1t27dGjwJuezAgQPatm2b\nhgwZ4vj9J598UvPnz2/wuYj79+9Xx44dNWXKFA0ePFjTp0/X+fPnHcd26dJFs2bNUvfu3dW1a1e1\nb99eI0eObLDH48ePKxQKSfr6h+b48eON/rsAU3jJZmO5lILPJrlEU0c2ATMlI5tucim5zybns0hH\nZNPOyIX3zpw5o6KiIi1atEht27a1fX/16tUKhUIqKCiQZVmyLMtxOzU1NaqsrNSMGTNUWVmpNm3a\nqLS01HHsyZMnVVZWpoMHD+rIkSM6c+aMli1bFlffjf0QAE1ZY7mUzMwmuURzRzYBM/l1Piu5zybn\ns0Dj0iGbSZ/kd+3aVYcOHYp+XVVVpa5du8YcX1NTo6KiIk2cOFFjx451HFNRUaFVq1YpKytLxcXF\nevfddzVp0iTbuG7duikzM1OFhYWSpKKiIlVWVjpuc926dcrKylKHDh2UkZGhcePGaePGjQ3+20Kh\nkI4dOyZJOnr0qDp16tTgeMAk8WTTTS4lM7JJLtHUkU3ATH5n020uJffZ5HwW6Yhs2iU0yXeziuFt\nt92mPXv26ODBg7p48aJWrFjR4AqFjz76qHJycjRz5syYY+bOnatDhw5p3759WrFihUaMGKHXX3/d\nNi4UCikzM1O7d++WJJWXlysnJ8dxm927d9emTZt04cIFWZal8vJyZWdn1xtz9V9yxowZoyVLlkiS\nli5d2uAJFpBKfmfTTS6lYLJJLtGUkM0lksgmzBNENt3mUnKfTc5n0dyQzSWSPGTT9RJ9V4ln1fy1\na9daffv2tXr37m298MILMbe5YcMGq2XLltbAgQOtgoICa9CgQdbatWsb7CMSiTS44uG2bduswsJC\na+DAgdaDDz5onTx5MubYkpISq3///lZeXp41adIk6+LFi9HvFRcXW7fccot1zTXXWJmZmdarr75q\nffnll9bdd99t9e3b17rnnnusEydONNgrkAp+Z9NLLi0rNdkkl2hKyCbZhJlMyGZjubQs99nkfBbN\nBdn0ns0WltXATQYN2LRpk+bMmaO1a9dKkkpLS9WiRQvNnj273rh0v6/H438v4BnZdIdsItXIZuPI\nJYJANhtHNhEEstm4WNls5XWDTqsYbtmyxevmmp3i4mItX7486DaQhshmw5566im9+OKLQbeBNJSK\nbL7yyiuO9RkzZvi6H0m64YYbJElfffWVrr32WklSr169bOOuPAE5evSoOnfurA8//ND3fgCvgjxu\nXs5HSUmJSkpKXL2mobFX5u3KcaNGjbKNXbduXVy9AqnGOa13Rq6u3xxs37496BYAOKioqAi6BQAA\nACBpPE/y4101P93k5eUF3QLSFNls2NChQ4NuAWmKbAJmIpuAmcimd54n+fGumg8gNcgmYKbmms2M\njAzXY52eRwwEzYRshsNh38fGs03ARCZks6nyfE9+RkaGXn75ZY0aNUp1dXWaOnWq7TEAAFKPbAJm\n8jubdXV1rsd++9vfttU6d+7sed+SdPr0aVtt27ZtCW0TCIIJx81kT/J79+5t+z735MN0JmQz2SZO\nnGir/du//VvC2/U8yZeke++9V7t27Uq4CQD+IpuAmcgmYCayCZiJbHrDwnsAAAAAADQTTPIBAAAA\nAGgmPE/yq6qqNGLECOXm5iovL0+LFy/2sy8AHpFNwExkEzAT2QTMRDa983xPfqtWrbRgwQIVFBTo\nzJkzuvXWWzVq1Cj179/fz/4AxIlsAmbyO5stWrRwPbZTp062Wm1tra0Wz0r5QHORiuPmrFmzfNtW\nQ2L9XnjllVdstS+//NJWW7lype89AV41p3PaeBbLfeyxx2y1eB8B7fmd/M6dO6ugoEDS14/Eyc7O\n1uHDh71uDoBPyCZgJrIJmIlsAmYim975ck/+gQMHtG3bNg0ZMsSPzQHwCdkEzEQ2ATORTcBMZDM+\nCU/yz5w5o6KiIi1atEht27b1o6dmYfv27UG3gDRHNp1VVFQE3QLSHNkEzEQ2ATORzfglNMmvqalR\nUVGRJk6cqLFjx/rVU7OQl5cXdAtIY2QztnjvaQL8RDYBM5FNwExk0xvPC+9J0qOPPqqcnBzNnDnT\nr35ccbpM4xe/+IWtlp+f7/j6vXv32mp9+/ZNvDHAEKnIZq9evWy1Vq3sv1J27drl+76d9lNTU+P7\nfgC/BXXcdFqMy6nmtDCQZVmO2/yHf/gHW+2nP/2ph+6A4CU7m3fffXdStutWy5b29/WWL19uq23a\ntMlWO3ToUFJ6AtwI6riZiH//93+31eJZLPeOO+5IuAfP7+RXVFTojTfe0G9/+1sNGjRIgwcP1ttv\nv51wQwASQzYBM5FNwExkEzAT2fTO8zv5Q4cOdXz8DoBgkU3ATGQTMBPZBMxENr3zZXV9AAAAAAAQ\nvIQn+XV1dRo8eLDGjBnjRz8AfEI2ATORTcBMZBMwE9mMX8KT/EWLFiknJ8ePXgD4iGwCZiKbgJnI\nJmAmshm/hFbXr6qq0po1a/TMM89owYIFfvXUqPfff9/VuFirGDqtCp6RkWGrcQ8ImqpUZNPpKRVO\n5s6da6s5Pc3i3nvvdXx9mzZtXO3HaQXwF1980Va77bbbHOtAKgR13HTL7Sr8klRaWmqrOT3V5pVX\nXrHV3B7HgVRJdjZXr17tWP/Lv/xLW619+/a+79+JU7afeeYZW+3v/u7vUtEO4Mj046aTRx55JKHX\nx7MSfywJvZP/5JNPav78+b40AsA/ZBMwE9kEzEQ2ATORTW88v5O/evVqhUIhFRQUKBKJxHyObrra\nvn170C0gTZFNu71792rfvn2SpI8//jjgbpCuyCZgJrJZ365du7R79+6g2wDIZgI8T/IrKiq0atUq\nrVmzRufPn9fp06c1adIkvf76637212Tl5eVpx44dQbeBNEQ27Xr16hW9Tee2225L6/8LBIdsAmYi\nm/X169dP/fr1i37961//OsBukM7IpneeL9efO3euDh06pH379mnFihUaMWIE/+GAAcgmYCayCZiJ\nbAJmIpveJbTwXio4LbqV6D0ZTq//+7//e1vt5z//eUL7AZqaeBagdBr7V3/1V7ZaJBKx1X74wx+6\n7slt3p3G/a//9b9c7wdoal5++WVb7fvf/37K9u+UOafFhpxqX375pa3WsWNHfxoDDPTOO+841h9/\n/HFbLVUL7zkZP368rcbCe0BsdXV1tlo8c1WnWxCeffbZhHqSfJrkDx8+XMOHD/djUwB8RDYBM5FN\nwExkEzAT2YxPQqvrAwAAAAAAcyQ0ya+urtb48eOVnZ2t3Nxcbd682a++ACSAbAJmIpuAmcgmYCay\n6U1Cl+vPnDlT9913n958803V1NTo3LlzfvUFIAFkEzAT2QTMRDYBM5FNb1pYHh84eOrUKQ0aNEh7\n9+5teAcJLpL3+eef22qdO3dOaJtO/+QpU6bYakuXLvW8j+LiYi1fvpznOSLlEsnm2LFjbbVYj4K8\n5pprHPd9tc8++8zVvlOpRYsWZBMp5/dx02mc0wJAJnLKX8uWLcklApGqc1onTgvvLV682Pf9uEU2\nYZIgs+lWotlwen1BQYGt9tFHH8W1f8+X6+/fv18dO3bUlClTNHjwYE2fPl3nz5/3ujkAPiGbgJnI\nJmAmsgmYiWx653mSX1NTo8rKSs2YMUOVlZVq06aNSktL/ewNgAdkEzAT2QTMRDYBM5FN7zzfk9+t\nWzdlZmaqsLBQklRUVKR58+b51lhTt3379qBbQJoim3aRSESRSCToNpDmyGZ95BKmIJv1kU2Ygmx6\n53mSHwqFlJmZqd27d6tv374qLy9XTk6On701aXl5eTHvZQaSiWzahcNhhcPh6Ndz5swJrhmkLbJZ\n39W5fP7554NrBmmNbNZHNmEKsumd54X3JOnDDz/UtGnTdOnSJWVlZem1115Tu3bt6u8gwYUQnBYR\nSnSbTv/k+fPn22qzZ892tb1p06bZarfffrumT5/OQiUIhNdsZmRk2GoPPvig4z7GjBljq333u9+1\n1YJeZM8JC+8hKMk+bjbln2tyiSCl4pzWLdMW1SSbCJJJ2XSybds2W23gwIGuXx9rsctEXi8l+Ai9\ngQMH6ve//30imwCQBGQTMBPZBMxENgEzkU1vPC+8BwAAAAAAzMIkHwAAAACAZiKhSf7ChQs1YMAA\n5efna8KECbp48aJffQFIANkEzEQ2ATORTcBMZNMbzwvvHTlyRMOGDdPOnTt1zTXX6Dvf+Y7uv/9+\nTZo0qf4OElwIwWlFz3/6p39KaJtO/+T//b//t6126tQpW628vNxWu//++221fv366f7772ehEqRc\nPNmcOXNmvVqfPn1s27v6dZe1bdvWVgty4ROnrL311lu2Wu/evZWfn082kXKpOG6WlJQ41p977jnP\n20wVFvdCUFJ1TpuIZCzG55S32tpaW61169ZkE4FoCtmcMGGCrXbXXXfZakOGDHF8fUFBQUL7T8rC\ne7W1tTp79qxatmypc+fOqUuXLolsDoBPyCZgJrIJmIlsAmYim954vly/S5cumjVrlrp3766uXbuq\nffv2GjlypJ+9AfCAbAJmIpuAmcgmYCay6Z3nd/JPnjypsrIyHTx4UO3atVNRUZGWLVumRx55xM/+\nmpQ9e/Zoz549kqQPPvgg4G6QruLJ5qZNm6Kfd+vWzfFy/eZgx44d2rFjhySpQ4cOAXeDdMVxs75I\nJKJIJBJ0GwDZvMp7772n9957L+g2ALKZAM+T/HXr1ikrKyt6wjxu3Dht3Lgxrf/Te/furd69e0v6\n+p78ZcuWBdwR0lE82bzjjjtS3V4gBgwYoAEDBkj6Oqe/+MUvAu4I6YjjZn3hcFjhcDj69Zw5c4Jr\nBmmNbNY3fPhwDR8+PPr1j3/84wC7QTojm955vly/e/fu2rRpky5cuCDLslReXq7s7Gw/ewPgAdkE\nzEQ2ATORTcBMZNM7z+/k33777SoqKtKgQYPUunVrDRo0SNOnT/ezN0nOKwK7XV0/1mqDTvW8vDxb\n7ezZs7aa06WFvXr1stVCoZCLDgH/xZPN73znO/W+jued/SBXMnXr+PHjtlrnzp0D6ARIzXEz1rvh\nw4YNs9XuvvtuX/cNNFWpOqdNhNO5q9NxONax2e1K/EVFRfE1BiRRU8jmG2+84aqWap4foed6BwlO\nBBJ5ZEg8k/wNGzbYak6T/J/97Ge22owZM2y1rl27qrCwkEeOwFgtWrTQxo0b69Wa8iTfKWu//OUv\nbbX8/HzdeeedZBPGSiRbsV77zjvv2GqmTfJ5hB5MZ9pxz0k8k3ynvD344IO2WllZGdmE0ZpCNpMl\nVjY9X64PAAAAAADM0ugkf+rUqQqFQsrPz4/WTpw4oVGjRqlfv34aPXq0qqurk9okADuyCZiJbAJm\nIpuAmcim/xqd5E+ZMkW/+c1v6tVKS0s1cuRI7dq1SyNGjNALL7yQtAYBOCObgJnIJmAmsgmYiWz6\nz9U9+QcPHtQDDzygjz76SJLUv39/vffeewqFQjp69KjC4bB27tzpvIMk3CMxbdo0W83p8R5//OMf\nHV//4osv2mqff/65rXby5ElbraamxtW48ePH67XXXuMeJiRVotk8d+5cvdr111+f9J794JSrCxcu\n2GqffPKJrdapUydlZmaSTSSVacdNt+JZB8cpQ06L5T7//POu900ukWxNnSGlMAAAIABJREFUNZtB\nI5tItqaaTacFnY8ePZqy/ft6T/7x48ejq8d37tzZcQVrAKlHNgEzkU3ATGQTMBPZTIwvC++l8182\nAZORTcBMZBMwE9kEzEQ249PKy4tCoZCOHTsWvXyiU6dOfvfVJNXU1Ki2tlaStHXr1oC7QTqKN5s/\n+clPop/fddddGj16dLJbDMQHH3ygP/zhD5Kkv/iLvwi4G6Qjjpt2kUhEkUgk6DaQ5sgmYCaymRhX\nk3zLsupd7z9mzBgtWbJEs2fP1tKlSzV27NikNdiUtGrVSq1aff1fOmjQIG3bti3gjtDcJZrNH/3o\nR8lu0QiFhYUqLCyU9PU9+QsXLgy4IzR3HDcbFw6HFQ6Ho1/PmTMnuGaQNsgmYCay6a9GF9575JFH\nFIlE9MUXXygUCmnOnDn61re+pfHjx+uzzz5Tjx49tHLlSrVv3955B0m4tKJDhw62WpcuXWy1HTt2\n+L5vJ06LlT300ENaunQpC5UgafzIZqwFta60fv16x/pdd92VUP9uOWXo008/tdX69OnjepstW7Yk\nm0gaE4+bTQW5RDKRTe/IJpKJbHoXK5uuVtdPRDIn+ZcuXVLr1q0lMckH4nV5kh+JROq9m3a1Kyf5\nW7du1aBBgyQ1PMlvbJvxjL2coSvHNTbJb2ybTPJhMk5WADORTcBMZNPOl4X3gnLp0qWgWwCavHju\niXV7C0o823Q7NhnbBAAAAJqbJj3JBwAAAAAA/w+TfAAAAAAAmokmeU9+U8I9TDAV2SSbMFM6Z5Nc\nwmRkEzAT2bRL+iQfAAAAAACkBpfrAwAAAADQTDDJBwAAAACgmWCSDwAAAABAM5GSSf7bb7+t/v37\nq2/fvpo3b17McVVVVRoxYoRyc3OVl5enxYsXN7jduro6DR48WGPGjIk5prq6WuPHj1d2drZyc3O1\nefPmmGMXLlyoAQMGKD8/XxMmTNDFixej35s6dapCoZDy8/OjtRMnTmjUqFHq16+fRo8ererq6gb7\nBUzjJpvx5lJKXTbJJZorsgmYKRnZdJNLyX02OZ9FOiKbV7GSrLa21urVq5d14MAB6+LFi9bAgQOt\nTz75xHHs559/bm3dutWyLMs6ffq01bdv35hjLcuyFixYYE2YMMF64IEHYo6ZPHmy9eqrr1qWZVmX\nLl2yqqurHccdPnzY6tmzp/XVV19ZlmVZDz30kLV06dLo99evX29t3brVysvLi9aefvppa968eZZl\nWVZpaak1e/bsmH0ApnGbzXhzaVmpyya5RHNENgEzJSubbnJpWe6yyfks0hHZtEv6O/lbtmxRnz59\n1KNHD7Vu3VoPP/ywysrKHMd27txZBQUFkqS2bdsqOztbhw8fdhxbVVWlNWvWaNq0aTH3ferUKa1f\nv15TpkyRJLVq1Uo33nhjzPG1tbU6e/asampqdO7cOXXp0iX6vWHDhummm26qN76srEyTJ0+WJE2e\nPFlvvfVWzG0DpnGbzXhyKaU2m+QSzRHZBMyUjGy6yaUUXzY5n0W6IZt2SZ/kHz58WJmZmdGvu3Xr\n1uBJyGUHDhzQtm3bNGTIEMfvP/nkk5o/f36Dz0Xcv3+/OnbsqClTpmjw4MGaPn26zp8/7zi2S5cu\nmjVrlrp3766uXbuqffv2GjlyZIM9Hj9+XKFQSNLXPzTHjx9v9N8FmMJLNhvLpRR8NsklmjqyCZgp\nGdl0k0vJfTY5n0U6Ipt2Ri68d+bMGRUVFWnRokVq27at7furV69WKBRSQUGBLMuSZVmO26mpqVFl\nZaVmzJihyspKtWnTRqWlpY5jT548qbKyMh08eFBHjhzRmTNntGzZsrj6buyHAGjKGsulZGY2ySWa\nO7IJmMmv81nJfTY5nwUalw7ZTPokv2vXrjp06FD066qqKnXt2jXm+JqaGhUVFWnixIkaO3as45iK\nigqtWrVKWVlZKi4u1rvvvqtJkybZxnXr1k2ZmZkqLCyUJBUVFamystJxm+vWrVNWVpY6dOigjIwM\njRs3Ths3bmzw3xYKhXTs2DFJ0tGjR9WpU6cGxwMmiSebbnIpmZFNcommjmwCZvI7m25zKbnPJuez\nSEdk0y6hSb6bVQxvu+027dmzRwcPHtTFixe1YsWKBlcofPTRR5WTk6OZM2fGHDN37lwdOnRI+/bt\n04oVKzRixAi9/vrrtnGhUEiZmZnavXu3JKm8vFw5OTmO2+zevbs2bdqkCxcuyLIslZeXKzs7u96Y\nq/+SM2bMGC1ZskSStHTp0gZPsIBU8jubbnIpBZNNcommhGwukUQ2YZ4gsuk2l5L7bHI+i+aGbC6R\n5CGbrpfou0o8q+avXbvW6tu3r9W7d2/rhRdeiLnNDRs2WC1btrQGDhxoFRQUWIMGDbLWrl3bYB+R\nSKTBFQ+3bdtmFRYWWgMHDrQefPBB6+TJkzHHlpSUWP3797fy8vKsSZMmWRcvXox+r7i42Lrlllus\na665xsrMzLReffVV68svv7Tuvvtuq2/fvtY999xjnThxosFegVTwO5tecmlZqckmuURTQjbJJsxk\nQjYby6Vluc8m57NoLsim92y2sKwGbjJowKZNmzRnzhytXbtWklRaWqoWLVpo9uzZ9cal+309Hv97\nAc/IpjtkE6lGNhtHLhEEstk4sokgkM3GxcpmK68bdFrFcMuWLV43p2uvvTb6eU1NjVq1+rq1//mf\n/7GN3bx5syTpt7/9rUaMGCFJjvdJ/OY3v4l+vmrVquhlG9OnT/fcp1vFxcVavnx50vcDXM3vbDY3\nTz31lF588cWg20AaIpuAmeLJZmZmpqqrq9WuXTtJUu/evW1jXnrppejnv/jFL/TYY4/pk08+cdze\nj370I0nSiRMnoo/P2r9/v7d/CNDMcNz0zsjV9QEAAAAAQPw8v5Mf76r56Wb79u1Bt4A0RTYbVlFR\nEXQLSFNkEzBTPNmsrq7WhQsXJNW/ChWA/zhueuf5nfx4V82Pq6mW7trq2bOn623269fPazue5OXl\npXR/wGXJzGZzMHTo0KBbQJoim4CZ4slmu3btoh/XXXddo9u+/FitxrjZFpBuOG565/md/IyMDL38\n8ssaNWqU6urqNHXqVNtjAC6rq6vz3KCTkSNHuhp3xx13OH6+Zs0a29gr79+/bNGiRR66A4IVTzb9\n9sQTT9hq3//+9221Nm3a2Gr/+I//6LjNy48OAZq6ILMJILZ4svmDH/yg3tdO57hX3zO8ZcsWHTx4\n0NX2JOnxxx931ffle/ivdMMNNziOvfLdUKCp4LjpnedJviTde++92rVrl1+9APAJ2QTMRDYBM5FN\nwExk0xsW3gMAAAAAoJlgkg8AAAAAQDPheZJfVVWlESNGKDc3V3l5eVq8eLGffQHwiGwCZiKbgJnI\nJmAmsuldC8uyLC8vPHr0qI4ePaqCggKdOXNGt956q8rKytS/f//6O2jRQh53kbBE9xuJRGy1qqoq\nW+273/2u4+tbtmwZ2L8d6SuebPrN70U2Jel//ud/bLV3333XVjt8+LCt9utf/9pWe+KJJ/TCCy+Q\nTaRckNlsKsglguB3Nv/iL/7CVmvVynkZrO9973u22rZt22y12267zVZ74403bLXBgwc77mf16tWO\ndbfIJoLQFI6b1dXVrsa1bdvWsZ6RkZHQ/mNl0/M7+Z07d1ZBQYGkr5vOzs52PMkGkFpkEzAT2QTM\nRDYBM5FN73y5J//AgQPatm2bhgwZ4sfmAPiEbAJmIpuAmcgmYCayGZ+EHqEnSWfOnFFRUZEWLVoU\n8zKEkpKS6OfhcFjhcDjR3RopEok4XuIPBMFNNtNFbW1t9FaC9evXB9wN0h3ZBMxENgEzkc34JTTJ\nr6mpUVFRkSZOnKixY8fGHHflJL85u/oPGM8//3xwzSCtuc1musjIyIje8/RXf/VX2rBhQ8AdIV2R\nTcBMZBMwE9n0JqHL9R999FHl5ORo5syZfvUDwAdkEzAT2QTMRDYBM5FNbzyvrl9RUaG77rpLeXl5\natGihVq0aKG5c+fq3nvvrb+DAFfXj8WpnwULFthqM2bMsNWuvfZaW23q1Km22pAhQ/TYY48Z929H\n8xdPNhNx5513Ou7bb04Zevvtt201p5X9V61aZavdc889Gj9+PNlEysWTzat/np1+XhNdkbddu3a2\nmtMx7ujRo46vd+ppwoQJrva9YsUK19sEks3v4+Ytt9xiqzmdZ0rSfffdZ6u1adPGVisrK7PVduzY\nYav90z/9k+N+nH5fOD0FIDc311bbsmUL2UQgUnVO65bTSvo33HCDq9fG6tEpW/Ec82Nl0/Pl+kOH\nDlVtba3XlwNIErIJmIlsAmYim4CZyKZ3vqyuDwAAAAAAgpfwJL+urk6DBw/WmDFj/OgHgE/IJmAm\nsgmYiWwCZiKb8Ut4kr9o0SLl5OT40QsAH5FNwExkEzAT2QTMRDbjl9Aj9KqqqrRmzRo988wzMRcU\naSpGjhxpq1VWVtpqf/mXf2mrrVy50lZjgRIEKRXZHD16dFK268bGjRttta+++spW++Uvf2mr3Xjj\njUnpCXDDbTavXmDHaWFJp/sU41mM78SJE67GxVosyKm+bNkyW83peBhr4T0gKH4eN3v27GmrjRs3\nznFs69atXW3T6fWxtulWYWGhrVZcXGyrbdmyJaH9AIkwab7ptMheoov+uX39zTffbKt98cUXMccn\n9E7+k08+qfnz56dsRUMA7pBNwExkEzAT2QTMRDa98fxO/urVqxUKhVRQUKBIJNLgO9clJSXRz8Ph\nsMLhsNfdGq22tjb6zsrWrVsD7gbpKp5spqNkPOYPcCOebJJbIHU4bta3a9cu7d69O+g2ALJ5lUuX\nLunSpUuuxnqe5FdUVGjVqlVas2aNzp8/r9OnT2vSpEl6/fXXbWOvnOQ3ZxkZGdFLJQcNGqQPP/ww\n4I6QjuLJZjoaOnSo3n///aDbQBqKJ5u8YwGkDsfN+vr166d+/fpFv169enWA3SCdkc36WrduXe8W\nn/Pnz8cc6/ly/blz5+rQoUPat2+fVqxYoREjRqTtfzhgErIJmIlsAmYim4CZyKZ3CS2811Q5vUOS\nn59vq/3sZz+z1Zwu9T19+rTjfpYsWRJ/c0ATMWDAgMD2/ZOf/CSwfQOpcPUliRMnTrSN+bd/+zdb\nzWmBvliScbWA0zadak59tmyZ8AN/gKS7eqHmdevW2cY4HR9jLbCXqqt23C7UyS1tgLRq1SrHepBX\n2TmdBzjNVS/zZZI/fPhwDR8+3I9NAfAR2QTMRDYBM5FNwExkMz782RwAAAAAgGaCST4AAAAAAM1E\nQpP86upqjR8/XtnZ2crNzdXmzZv96gtAAsgmYCayCZiJbAJmIpveJHRP/syZM3XffffpzTffVE1N\njc6dO+dXXynntJDCE0884eq1n3/+ua12/fXXJ9wT4FUqstmqlb/rdsZ69un//b//19f9AEHymk2n\nfFRVVdlq3bp1S7jHVOARgTCN22xu3bq13tff/OY3bWM6dOhgqwX9M++0/08//dRW69OnTyraAVxL\n9jltx44dbbW//du/9XUffoj30eyez9JPnTql9evXR1eQb9WqlW688UavmwPgE7IJmIlsAmYim4CZ\nyKZ3ni/X379/vzp27KgpU6Zo8ODBmj59us6fP+9nbwA8IJuAmcgmYCayCZiJbHrneZJfU1OjyspK\nzZgxQ5WVlWrTpo1KS0sdx5aUlEQ/IpGI110ab+PGjXrppZf00ksvxfy/AJItnmymI54BjKCQzfoi\nkUi98wMgKPFk89y5c9GPS5cupbhTIL1w3KzvxIkT2r9/f/SjIZ4v1+/WrZsyMzNVWFgoSSoqKtK8\nefMcx6bLwfvOO+/UnXfeKenre/J/+tOfBtwR0lE82UxHQ4cO1fvvvx90G0hDZLO+cDiscDgc/XrO\nnDnBNYO0Fk8227Rpk8rWgLTGcbO+m266STfddFP06wMHDsQc63mSHwqFlJmZqd27d6tv374qLy9X\nTk6O180Zye0iKbfcckuSOwHcS1U2f/KTn9hqY8aM8X0/06dP932bQBDS4bgJNEXxZPPqBbk+/vhj\n25im8oeA+++/P+gWgAYFddxMxkKZsRaY3rFjh602c+ZMW+3dd9+Na38JLY+9ePFiTZgwQZcuXVJW\nVpZee+21RDYHwCdkEzAT2QTMRDYBM5FNbxKa5A8cOFC///3v/eoFgE/IJmAmsgmYiWwCZiKb3nhe\neA8AAAAAAJgloUn+woULNWDAAOXn52vChAm6ePGiX30BSADZBMxENgEzkU3ATGTTG8+T/CNHjujn\nP/+5Kisr9dFHH6mmpkYrVqzwszcAHpBNwExkEzAT2QTMRDa9S+ie/NraWp09e1YtW7bUuXPn1KVL\nF7/6alKSsQIjkIhUZPPTTz+11X73u9/ZanfddZet5rTCKI+1Qzrwms1WreyH665du/rdXlI45f3E\niRMBdALE5jabVy/65bQydocOHZLSo992794ddAtAo5J9TvvnP//ZVhs7dqzj2LKyMlvN6Rj32Wef\n2WrV1dWO27zvvvtstZqaGsex8fD8Tn6XLl00a9Ysde/eXV27dlX79u01cuTIhBsCkBiyCZiJbAJm\nIpuAmcimd57fyT958qTKysp08OBBtWvXTkVFRVq2bJkeeeQR29iSkpLo5+FwWOFw2OtujRaJRBSJ\nRIJuA2kunmymo4qKiqBbQJoim/Vt2LCBPMIIiZzTduzYMYWdAumF46Z3nif569atU1ZWVvSSpHHj\nxmnjxo2N/kJszq7+A8acOXOCawZpK55spqOhQ4dyawACQTbrGzZsmIYNGxb9+qc//WmA3SCdJXJO\n63S5PgB/cNz0zvPl+t27d9emTZt04cIFWZal8vJyZWdn+9kbAA/IJmAmsgmYiWwCZiKb3nl+J//2\n229XUVGRBg0apNatW2vQoEGaPn26n70B8CBV2Tx58qSt9uMf/9hW++EPf2ir5ebm2mqLFi3ypzHA\nUIlk8/XXX7fVlixZklA/TosFvfLKK65f//3vfz+h/QOmiCebVy+2PGDAgFS06JpTriXp2muvTXEn\nQOKCmm9u2bLFsf7YY4/Zav/n//wfW622ttZWe+ONNxy3eenSJVvt2LFjjbXYqIRW13/uuef03HPP\nJdwEAH+RTcBMZBMwE9kEzEQ2vfF8uT4AAAAAADALk3wAAAAAAJqJRif5U6dOVSgUUn5+frR24sQJ\njRo1Sv369dPo0aNVXV2d1CYB2JFNwExkEzAT2QTMRDb918KKtULH/2/Dhg1q27atJk2apI8++kiS\nNHv2bN188816+umnNW/ePJ04cUKlpaXOO2jRIuYiIM1dOv/bkXx+ZDMVcnJybLX9+/fbaufPn09F\nO3rqqaf04osvkk0kTaqy+dBDD9lqv/rVr7w3LudFu5wWz5SkLl262GozZ850tc0PP/zQVhs0aBC5\nRFL5kc0333yzXs0pB061b37zmwn17pSNp59+2lZ78cUXE9pPPPsH/NJUzmk7d+5sq40ZM8ZWu/zI\nvytt2rTJcZuffvqprXb48GHXPcXKZqPv5A8bNkw33XRTvVpZWZkmT54sSZo8ebLeeust140A8AfZ\nBMxENgEzkU3ATGTTf57uyT9+/LhCoZCkr/+icfz4cV+bAuAN2QTMRDYBM5FNwExkMzEJPULvssYu\nkSgpKYl+Hg6HFQ6H/ditcSKRiCKRSNBtAFGpunypKamoqAi6BYBsSvrggw/0wQcfBN0GUE9j2Vy5\ncmX089zcXMdL8wH4j+NmfDxN8kOhkI4dO6ZQKKSjR4+qU6dODY6/cpLfnF39B4w5c+YE1wzSUrzZ\nTEdDhw7V+++/H3QbSDNk066wsFCFhYXRr//lX/4lwG6QruLNptNaGAD8x3EzMa4m+ZZl1bupf8yY\nMVqyZIlmz56tpUuXauzYsUlr0DROixvk5eXZavfdd18q2kGaawrZ/OMf/xh0C0DKpSKbVy8AJiW+\n8J7TOyWxFjpavHixrbZo0SJb7a677rLV1q5d66E7IHGJZvMf/uEfbNu72ve+9z1b7Uc/+lFcPV7t\npZdestX+/Oc/u94mYLqmcE579OhRW23VqlW2WnZ2tuttXnfddQn1FEuj9+Q/8sgjuvPOO7V79251\n795dr732mn74wx/qnXfeUb9+/VReXh5z5V0AyUM2ATORTcBMZBMwE9n0X6Pv5C9btsyxvm7durh3\nFolEXN2P7/e4oLcJJIOf2QTgnyCyaVmWq/sVk3GM+/TTT9WnTx9X2/zggw/qXaIPpJJf2Tx37pza\ntGnjauz+/fvVs2fPRsfFk829e/eqV69ersYCTUFzOKf96quvdO2117oae+LECdvTBPzmaXV9r9wu\nSuf3uKC3CQCACZJxjNuzZ4/rbbLQHpqD8+fPux67f/9+V+PiyebevXtdjwWQGhcvXnQ99uTJk0ns\n5GspneQDAAAAAIDk8eUReunuW9/6lq3mtBgfYJrvfe972rp1qwYNGuRqvNuxJm+TS4XRFLjJ5pWX\n58eTD79cXljogw8+aHCRoQ4dOkQ/v/7669WhQwfl5+cnvT8gGcaNG6cNGzZo2LBhMcdc+fP98ccf\n+/LznpubG/1869atys3NbfRyX7+Or0uWLHHdJxCUoM5p27VrJ0natGmT7rjjDknSLbfc0uA233nn\nHd1zzz2SpC+//NL2/ePHj7vad0PZbGE5LeHpo3R/pmGS/3sBz8gm2YSZ0jmb5BImI5uAmcimXdIn\n+QAAAAAAIDW4Jx8AAAAAgGaCST4AAAAAAM1ESib5b7/9tvr376++fftq3rx5McdVVVVpxIgRys3N\nVV5enhYvXtzgduvq6jR48GCNGTMm5pjq6mqNHz9e2dnZys3N1ebNm2OOXbhwoQYMGKD8/HxNmDCh\n3qMQpk6dqlAoVG/xlBMnTmjUqFHq16+fRo8ererq6gb7BUzjJpvx5lJKXTbJJZorsgmYKRnZdJNL\nyX02OZ9FOiKbV7GSrLa21urVq5d14MAB6+LFi9bAgQOtTz75xHHs559/bm3dutWyLMs6ffq01bdv\n35hjLcuyFixYYE2YMMF64IEHYo6ZPHmy9eqrr1qWZVmXLl2yqqurHccdPnzY6tmzp/XVV19ZlmVZ\nDz30kLV06dLo99evX29t3brVysvLi9aefvppa968eZZlWVZpaak1e/bsmH0ApnGbzXhzaVmpyya5\nRHNENgEzJSubbnJpWe6yyfks0hHZtEv6O/lbtmxRnz591KNHD7Vu3VoPP/ywysrKHMd27txZBQUF\nkqS2bdsqOztbhw8fdhxbVVWlNWvWaNq0aTH3ferUKa1fv15TpkyRJLVq1Uo33nhjzPG1tbU6e/as\nampqdO7cOXXp0iX6vWHDhtkeVVJWVqbJkydLkiZPnqy33nor5rYB07jNZjy5lFKbTXKJ5ohsAmZK\nRjbd5FKKL5uczyLdkE27pE/yDx8+rMzMzOjX3bp1a/Ak5LIDBw5o27ZtGjJkiOP3n3zySc2fP7/B\nRybs379fHTt21JQpUzR48GBNnz5d58+fdxzbpUsXzZo1S927d1fXrl3Vvn17jRw5ssEejx8/rlAo\nJOnrH5rLzzQEmgIv2Wwsl1Lw2SSXaOrIJmCmZGTTTS4l99nkfBbpiGzaGbnw3pkzZ1RUVKRFixap\nbdu2tu+vXr1aoVBIBQUFsiwr5vMBa2pqVFlZqRkzZqiyslJt2rRRaWmp49iTJ0+qrKxMBw8e1JEj\nR3TmzBktW7Ysrr7T+RmNaP4ay6VkZjbJJZo7sgmYya/zWcl9NjmfBRqXDtlM+iS/a9euOnToUPTr\nqqoqde3aNeb4mpoaFRUVaeLEiRo7dqzjmIqKCq1atUpZWVkqLi7Wu+++q0mTJtnGdevWTZmZmSos\nLJQkFRUVqbKy0nGb69atU1ZWljp06KCMjAyNGzdOGzdubPDfFgqFdOzYMUnS0aNH1alTpwbHAyaJ\nJ5tucimZkU1yiaaObAJm8jubbnMpuc8m57NIR2TTLumT/Ntuu0179uzRwYMHdfHiRa1YsaLBFQof\nffRR5eTkaObMmTHHzJ07V4cOHdK+ffu0YsUKjRgxQq+//rptXCgUUmZmpnbv3i1JKi8vV05OjuM2\nu3fvrk2bNunChQuyLEvl5eXKzs6uN+bqv+SMGTNGS5YskSQtXbq0wRMswDTxZNNNLqVgskku0dyQ\nTcBMfmfTbS4l99nkfBbpiGw6cL1En4O1a9da/fr1s/r06WOVlpY2OK5v375W7969rRdeeCHmuA0b\nNlgtW7a0Bg4caBUUFFiDBg2y1q5d22APkUikwRUPt23bZhUWFloDBw60HnzwQevkyZMxx5aUlFj9\n+/e38vLyrEmTJlkXL16Mfq+4uNi65ZZbrGuuucbKzMy0Xn31VevLL7+07r77bqtv377WPffcY504\ncaLBXoFU8TObXnJpWanJJrlEU0M2ySbMFHQ2G8ulZbnPJuezaE7IprdstrCsBm4yaEBdXZ369u2r\n8vJydenSRbfddptWrFih/v371xuX7vf1ePzvBTwjm+6QTaQa2WwcuUQQyGbjyCaCQDYbFyubni/X\nj+fReOmouLg46BaQpshmw5566qmgW0CaiieblmXpueeei1665/RRV1cX/Xj22WdVV1env/mbv3H8\nuPyaxrZ55YfbsV76PHz4sO0DCArHTcBMTTWb/fr1s31kZmZGP2688cbo58nieZLv9dF4AJKLbAJm\nIpuAmcgmYCay6V2roBtorrZv3x50CwAcVFRUBN0C0KiSkhJFIhGVlJQoHA4rHA4H3ZLvNm7cqPff\nfz/oNgAAaHY8T/LjfTReusnLy9OOHTuCbgNpiGw2bOjQoUwsEIh4snl5ku92cu/3uFRs884779Sd\nd94Z/XrBggWutwP4ieMmYKbmms1rr7026fvwvPBebW2t+vXrp/Lyct1yyy26/fbbtXz5ctujANJ1\nIYTi4mItX76chUqQck0hm3/6059stSlTpjiO/fWvf+3rvp966imRppKbAAAgAElEQVS9+OKLZBMp\nF082vf58xnqdU96dxv7yl7+01b75zW86bnPUqFG2mtMjiT766CNb7d///d9ttczMTHKJQDSF42bQ\nyCaCYFo2hw0bZqu99957ttrjjz9uq8W6krR9+/authlLrGx6fic/IyNDL7/8skaNGqW6ujpNnTrV\n9h8OIPXIJmAmsgmYiWwCZiKb3iV0T/69996rXbt2+dULAJ+QTcBMZBMwE9kEzEQ2vfG8uj4AAAAA\nADCL50l+VVWVRowYodzcXOXl5Wnx4sV+9gXAI7IJmIlsAmYim4CZyKZ3ni/Xb9WqlRYsWKCCggKd\nOXNGt956q0aNGqX+/fv72R+AOJFNwExkEzAT2QTMRDa98zzJ79y5szp37ixJatu2rbKzs3X48GH+\n04GApSqbdXV1nl/rtBJoPKvod+rUyVY7deqUrXbhwoX4GgOSKBXZjGeFYaex06dPd/361157zVb7\n+c9/7uq18ewHSDbOaQEzBZXNWI+G/e1vf2urOR1LX375ZVvt4MGDjtv87ne/G19zLvlyT/6BAwe0\nbds2DRkyxI/NAfAJ2QTMRDYBM5FNwExkMz4Jra4vSWfOnFFRUZEWLVqktm3b+tFTs7B9+/agW0Ca\nI5vOYj2nFEgVN9ksKSmJfh4Oh2O+q9CUffHFF/ryyy+DbgOI4rgJmIlsxi+hSX5NTY2Kioo0ceJE\njR071q+emoW8vDzt2LEj6DaQpshmbEOHDtX7778fdBtIU26zeeUkv7m6+eabdfPNN0e/3rNnT4Dd\nIN1x3ATMRDa9Sehy/UcffVQ5OTmaOXOmX/0A8AHZBMxENgEzkU3ATGTTmxaW0wpYLlRUVOiuu+5S\nXl6eWrRooRYtWmju3Lm699576+8gjkWAmpPi4mItX77ccYExIJkSyabTO2lZWVmO+3GbbacM/PjH\nP7bV5syZ4/h6pwX+Wra0/33y2WefdVW7/HqyiVSLJ5sm/XzG6uWb3/ymrXbo0KGk7AtIpqZ6Trti\nxQpbbdiwYY5ja2trbbXPPvvMVrvy6prLsrOzySYCkYps/s3f/I2t9uqrrzqOvbwIoBexMrRz505b\nbfz48bbaxx9/HNd2PV+uP3ToUMdfGACCRTYBM5FNwExkEzAT2fTOl9X1AQAAAABA8BKe5NfV1Wnw\n4MEaM2aMH/0A8AnZBMxENgEzkU3ATGQzfglP8hctWqScnBw/egHgI7IJmIlsAmYim4CZyGb8EnqE\nXlVVldasWaNnnnlGCxYs8KsnAAlym02nRe2uloyFhp577jnX+3HTYyymLZIENKfjZqKL7AEmMSmb\nv/jFL2w1pwW//vqv/9pWu+GGGxy3uW/fPlvt/PnzttqpU6fctAikTLKz+cEHH9hqoVDI9/3EOift\n37+/rVZYWGirxVp4L5aE3sl/8sknNX/+fE6kAcOQTcBMZBMwE9kEzEQ2vfE8yV+9erVCoZAKCgpk\nWRaP1gAMQTYBM5FNwExkEzAT2fTO8+X6FRUVWrVqldasWaPz58/r9OnTmjRpkl5//XU/+2uytm/f\nHnQLSFPxZLOkpCT6eTgcVjgcTl2jKRSJRBSJRIJuA2mObAJm4py2vj/84Q+qrKwMug2AbCagheXD\nn0Tee+89vfTSS1q1apV9B2l6aUVxcbGWL1/OX5wQqMaymYp78p0y0LKl/SKiWPtJ5PmosbbZokUL\nsolANZZNk34+Y/XilONk7QtIFRPOaVN1T/7+/ftttRtvvNFWGzJkCNlE4JKVzW984xu22rFjxxzH\nJuN3gFO2pkyZYqstXbrU9eulBBfeA9C0JfLLyumXypIlS2w1p7+2xrOYXrr+oRAwXUVFha22aNEi\nW23v3r222h/+8Iek9ASYqnv37o71/Px8W+1Pf/qTreZ03Lx06ZKttnr1asf93H///bZaVlaWrTZz\n5kzH1wPN1ciRI221oM89V65cmfA2fJnkDx8+XMOHD/djUwB8RDYBM5FNwExkEzAT2YyP/9faAQAA\nAACAQDDJBwAAAACgmUhokl9dXa3x48crOztbubm52rx5s199AUgA2QTMRDYBM5FNwExk05uEVtf/\n3ve+p+HDh2vKlCmqqanRuXPnbKtyBr1wQVBYXR9BcptNNz+fscZcuHDBVrvuuutc9Rf07wXTVi9H\n+vAzm6kSqxen1YeXL19uqzkt5DVmzBhbbfXq1Ub9u5FevJ7T/uu//quttnPnTltty5Ytjvvt1KmT\nrTZ48GBbzWkl/dtvv91xm07cHndjPRGHbCIoyZ5vZmRk2Go1NTWetxcvt0+hiuf1UgIL7506dUrr\n16+PrqbdqlUrx8duAEgtsgmYiWwCZiKbgJnIpneeL9ffv3+/OnbsqClTpmjw4MGaPn26zp8/72dv\nADwgm4CZyCZgJrIJmIlseud5kl9TU6PKykrNmDFDlZWVatOmjUpLS/3srUnbvn170C0gTcWTzZKS\nkuhHJBJJbaMpFIlE6v1bgSCQzfq++OIL7d69O/oBBIVz2vo4ZsIUZNM7z5frd+vWTZmZmSosLJQk\nFRUVad68eb411tTl5eVpx44dQbeBNBRPNtPl4B0OhxUOh6Nfz5kzJ7hmkLbIZn0333yzbr755ujX\nn376aYDdIJ1xTlvf1cfM559/PrhmkNbIpnee38kPhULKzMyM/vW9vLxcOTk5vjUGwBuyCZiJbAJm\nIpuAmcimdwmtrv/hhx9q2rRpunTpkrKysvTaa6+pXbt29XfA6vpBt4I05Dabifx8Or22qeTdtNXL\nkT5SkU2/xepl06ZNttodd9xhqzn9XmAFb5jGbTavfjLEwYMHbdvq3r27rdajRw/H/bZp08ZWc3s5\ncqqOuab9TkJ6CWK++S//8i+O9enTp/u6H8nA1fUlaeDAgfr973+fyCYAJAHZBMxENgEzkU3ATGTT\nG8+X6wMAAAAAALMkNMlfuHChBgwYoPz8fE2YMEEXL170qy8ACSCbgJnIJmAmsgmYiWx643mSf+TI\nEf385z9XZWWlPvroI9XU1GjFihV+9gbAA7IJmIlsAmYim4CZyKZ3Cd2TX1tbq7Nnz6ply5Y6d+6c\nunTp4ldfABKQimw2lUX2AJMEddx0Wphn5cqVttrDDz+c0H6cfi/U1dW5GgcEyW02W7duXe/rvXv3\n2sbccMMNttpdd93luL2NGzfaauQD+H+COG7+3d/9nWN98+bNttq//uu/utpmrAXyMjIybLVOnTrZ\nasePH3e1n8s8v5PfpUsXzZo1S927d1fXrl3Vvn17jRw50uvmAPiEbAJmIpuAmcgmYCay6Z3nSf7J\nkydVVlamgwcP6siRIzpz5oyWLVvmZ28APCCbgJnIJmAmsgmYiWx65/ly/XXr1ikrK0sdOnSQJI0b\nN04bN27UI4884ltzTdn27duDbgFpKp5slpSURD8Ph8MKh8Mp6jK1IpGIIpFI0G0gzZHN+sglTBFP\nNv/4xz9GP//GN76Rsh5T6f9j796jojrv9YE/4CWJIYkicZCbERW5ONyiSapWp8SoTSumFpMgRzho\nDr3YVWOTalfTNpA/DMRWo81Zp7djxPYoSU7TYOsljdRJFYN6giTYXIw3FAzSKKJ4w4H394c/p8Le\nA3vv2TP7Zeb5rMVazJeXvb+4eNz7ZfZ+N7NJsuB80zjDk/y4uDjU1NTg6tWruO2221BVVYVJkyaZ\n2Vu/ZrfbcejQIavboCCkJ5u3TiQCWc9JUklJiXXNUNBiNrtjLkkWerKZnJzs5+78j9kkWXC+aZzh\nSf4DDzyAnJwcZGRkYNCgQcjIyEBRUZGZvRGRAcwmkZxky6a3i+ypUVtYaMWKFYraunXrTN83kVF6\nsvn44493ez158mTFmLvvvltRS0pKUt1eTEyMgY6JgoNsx83169drqnlL7yJ7akKEp6X+TBKsK4Tm\n5uZi8+bNHldSJLJaSEhI0P5+BvPPTvLzxe+n2vZCQw0vy6PL8uXLFTW1Sf7Vq1eZS5JaSEgIXnvt\ntW61xsZGxTg9k/xTp04par74A5w3eMwk2QXrfBPwvGq/f47wRERERERERORznOQTERERERERBYg+\nJ/mLFy+GzWZDamqqu9ba2oqZM2di/PjxmDVrFtra2nzaJBEpMZtEcmI2ieTEbBLJidk0X5/35O/Z\nswdhYWHIz8/Hhx9+CODGIjrDhw/H8uXLUVZWhtbWVpSWlqrvoB/fI9HZ2amoDRgwQFHr6upS/f7Q\n0FDew0Q+Y0Y2g/X3M5h/dvI9M7LZ89ipdpyx8j57APjss88Utd/85jeKWkVFhaKmdh8y4PneQiIz\nmJFNT+d8t7p8+bKidvToUdWx165dU9QmTpyoum+r8JhJvhbM801vGb4nf+rUqRg2bFi3WmVlJQoK\nCgAABQUFeOutt0xokYj0YDaJ5MRsEsmJ2SSSE7NpPkN/8m9paYHNZgMAREZGmrLMPxF5j9kkkhOz\nSSQnZpNITsymdwaasZFgvkTiVk6nE06n0+o2iNz6ymZxcbH7c4fDAYfD4duGLMJskmz6yiYvjSWy\nBo+bPGaSnDjf1MfQJN9ms+HMmTOw2Wxobm7GiBEjzO6rX+r5n/0LL7xgXTMUlPRm89aTlUDWM5sl\nJSXWNUNBSW82eTJD5B88birxmEky4HzTO5ou1xdCdHtXITs7Gxs2bAAAlJeXY+7cuT5pjoh6x2wS\nyYnZJJITs0kkJ2bTXH2urr9gwQI4nU6cPXsWNpsNJSUleOyxxzB//nycOnUKo0aNwuuvv46hQ4eq\n78DCdyO0rICql9aVi3Nzc7F582Zeckk+Y0Y277zzzm619vZ2r3pS+30/fPiwojZ+/Hiv9uMtrhRM\nvmRGNnfu3Nmt9t577ynGHThwQFFTGwcAZ86cUdTUMqDnCTJq1Lap1tPUqVM1fz+RWczIZs/fUbXf\nWbUV80+cOKG6zSNHjihqZ8+eVdTy8/MVNbVsqmXYWzxmkq/15/mm1Txls8/L9Tdt2qRa73kCIjun\n06npvimt44isZlY2Ozs7NZ8U+CJHVm6TyBfMymZdXR3S09M1jf3iiy8QERHR5zhfZEPPNmtra5GZ\nmWnq/om0Miuben7n//73v2PatGl9jquvr4fdbjd1/zz3pf4iUOabMvHfA3UtpnUBES40QsGms7NT\n81hf5MjKbRLJ7IMPPtA89osvvtA0zhfZ0LPNgwcPmr5/In/T8zv/97//XdO4+vp60/fPc1+i4BU0\nk3wiIiIiIiKiQGfKI/T6MnbsWAA37jEaPnx4n+PNHmemmz9LX/u/+VxHIpmNGTMGzc3NiIyM9Nk+\nBg0a5LNtEwWqO+64A4MGDcIdd9wBABg2bJhizMiRI92fNzU1YeTIkRg9erRX+9V6jNPj9ttvd38+\ncOBA3H777d32c5PavclE/VHP+4NDQkI8HguHDBkC4Max8ubnHR0dvm2QKACNHTtW13HLynmpWdvs\n7bjZ58J73grmhRAALiJE8mI2mU2SUzBnk7kkmTGbRHJiNpV8PsknIiIiIiIiIv/gPflERERERERE\nAYKTfCIiIiIiIqIAwUk+ERERERERUYDwyyR/x44dSExMREJCAsrKyjyOa2xsRFZWFlJSUmC327Fu\n3bpet9vV1YXMzExkZ2d7HNPW1ob58+cjKSkJKSkp2Ldvn8exa9aswYQJE5Camoq8vLxuq5suXrwY\nNpsNqamp7lpraytmzpyJ8ePHY9asWWhra+u1XyLZaMmm3lwC/ssmc0mBitkkkpMvsqkll4D2bPJ8\nloIRs9mD8LHOzk4xZswYceLECdHR0SHS0tLExx9/rDr2888/FwcPHhRCCHHx4kWRkJDgcawQQqxe\nvVrk5eWJOXPmeBxTUFAg1q9fL4QQ4vr166KtrU11XFNTkxg9erS4du2aEEKIxx9/XJSXl7u/vnv3\nbnHw4EFht9vdteXLl4uysjIhhBClpaVixYoVHvsgko3WbOrNpRD+yyZzSYGI2SSSk6+yqSWXQmjL\nJs9nKRgxm0o+fyd///79GDduHEaNGoVBgwbhySefRGVlperYyMhIpKenAwDCwsKQlJSEpqYm1bGN\njY3Ytm0bnnrqKY/7vnDhAnbv3o3CwkIAN57Pe/fdd3sc39nZiUuXLsHlcuHy5cuIiopyf23q1KmK\n5xRXVlaioKAAAFBQUIC33nrL47aJZKM1m3pyCfg3m8wlBSJmk0hOvsimllwC+rLJ81kKNsymks8n\n+U1NTYiNjXW/jomJ6fUk5KYTJ06grq4ODz74oOrXly1bhlWrVvX6XMTjx48jIiIChYWFyMzMRFFR\nEa5cuaI6NioqCs888wzi4uIQHR2NoUOHYsaMGb322NLSApvNBuDGL01LS0ufPxeRLIxks69cAtZn\nk7mk/o7ZJJKTL7KpJZeA9mzyfJaCEbOpJOXCe+3t7cjJycHatWsRFham+PrWrVths9mQnp4OIQSE\nEKrbcblcqK2txZIlS1BbW4shQ4agtLRUdez58+dRWVmJhoYGnD59Gu3t7di0aZOuvvv6JSDqz/rK\nJSBnNplLCnTMJpGczDqfBbRnk+ezRH0Lhmz6fJIfHR2NkydPul83NjYiOjra43iXy4WcnBwsXLgQ\nc+fOVR1TXV2NLVu2ID4+Hrm5udi1axfy8/MV42JiYhAbG4uJEycCAHJyclBbW6u6zZ07dyI+Ph7h\n4eEYMGAA5s2bh7179/b6s9lsNpw5cwYA0NzcjBEjRvQ6nkgmerKpJZeAHNlkLqm/YzaJ5GR2NrXm\nEtCeTZ7PUjBiNpW8muRrWcVw0qRJOHLkCBoaGtDR0YGKiopeVyhctGgRkpOTsXTpUo9jVq5ciZMn\nT+LYsWOoqKhAVlYWNm7cqBhns9kQGxuLw4cPAwCqqqqQnJysus24uDjU1NTg6tWrEEKgqqoKSUlJ\n3cb0/EtOdnY2NmzYAAAoLy/v9QSLyJ/MzqaWXALWZJO5pP6E2dwAgNkk+ViRTa25BLRnk+ezFGiY\nzQ0A9GczRPR2/UEvurq6kJCQgKqqKkRFRWHSpEmoqKhAYmJi9x0E+SU/Bv95iQxjNrVhNsnfmM2+\nMZdkBWazb8wmWYHZ7JunbBp+J1/Pqvk3/yrx/PPPuz/v7cPscT3HdnV1KT6qqqpQVVWF/Px89+fe\nyM3N9er7iYzSk81g9Oyzz1rdAgUpf2QzNDRU9cMbUVFRqh8HDhzAgQMH8B//8R/uz9WOry6Xy/3x\n05/+FC6Xy6SflsgcerJ57NgxfP/738exY8dw7Ngxy89p/TGOyCpmHzfvvPNO98egQYNw5513ej2H\n/OY3v4lvfvObSEpKcn8uA8NHfqOr5hORbzGbRHJiNonkxGwSyYnZNG6gP3ZSXFwMAHA6nXA6nXA4\nHP7YraXq6+utboGIVFRXV1vdAhER9VMvv/wyampq8PLLL+Ohhx7C6NGjrW7JdDfP14mo/zI8ydez\niuGtk3wtE3ytfwTQ88cCrWPT0tI0b7M3drsdhw4dMmVbRHrofaJFsJkyZQree+89q9ugIBSo2bz/\n/vs1j50+fboPOyEyRk82n376adTU1OChhx7StG0rz2mNjnM4HN1qJSUl2hojMpkvj5sDBgzQNE5r\nju69914vujGf4cv19a6aD/j+PyUzxqanp2veJpGMjGSTiHwvULOpZ5IfDFfyUf+jN5taJ/hA/5zk\nE8nCl8fNQJ/kG34nf8CAAXjllVcwc+ZMdHV1YfHixYrHANzUc9Gfrq4uo7s1hdoKjPwPjgKFnmya\n7Z577lHUOjs7NX1ve3u72e0QScXsbKqdoBw4cEB1rNriWTk5OYpaa2uronb77bdr7knt+BrMqx5T\n/6Anm4sWLer2evHixYoxCxYsUNSOHj2quj21Y+TQoUMVtcjISNXvJwpkZh83Y2JiTOzuhnnz5ilq\nf/zjH03fj15e3ZM/e/ZsfPrpp2b1QkQmYTaJ5MRsEsmJ2SSSE7NpjHfP1SEiIiIiIiIiaRie5Dc2\nNiIrKwspKSmw2+1Yt26dmX0RkUHMJpGcmE0iOTGbRHJiNo0zfLn+wIEDsXr1aqSnp6O9vR33338/\nZs6cicTERDP7IyKdmE0iOTGbRHJiNonkxGwaZ3iSHxkZ6V4EJCwsDElJSWhqalL9R1db8Ec2XBiI\nAoWebJqtra1NUVNbkOTjjz9W1A4fPqy6Ta0L9xHJzuxsXr9+3at+jhw5oqi9+OKLipqn46PWVfV7\nLr4LqC/AqzaOyB/0ZHPUqFHdXk+YMEEx5p///KeiNnbsWK963LNnj6ZxU6dO9Wo/RDIx+7ipdm//\nE088oai99tprqt+vdjx88sknFTW1hffefPNNLS2axpQj6okTJ1BXV4cHH3zQjM0RkUmYTSI5MZtE\ncmI2ieTEbOrj1er6wI3HXuXk5GDt2rUICwszo6eAUF9fb3ULFOSYTXXV1dVWt0BBjtm8wel0wul0\nWt0GkZuWbNbV1bk/D9TH2jGbJBseN/XzapLvcrmQk5ODhQsXYu7cuWb1FBDsdjsOHTpkdRsUpJhN\nz6ZMmYL33nvP6jYoSDGb/+JwOOBwONyvX3jhBeuaoaCnNZvp6el+7MoaPbNZUlJiXTMU9HjcNMar\ny/UXLVqE5ORkLF261Kx+iMgEzCaRnJhNIjkxm0RyYjaNMfxOfnV1Nf7nf/4HdrsdGRkZCAkJwcqV\nKzF79mzFWLUFdqykthAgF/yhQKEnm2a7fPmyonbbbbcpar/73e8Utc8++0x1my6XS1F7+eWXDXRH\nZC2zs+ntgrFq3//jH//YL/vnYrckEz3ZHDlyZLfXw4cPV4wZMWKEoqbnd17tPPXee+9V1Pbu3auo\nceE9CiT+OKc9efKkV9+vNod84403FLWioiLV7//v//5vr/bvieFJ/pQpU7jqNZGEmE0iOTGbRHJi\nNonkxGwax7eviYiIiIiIiAIEJ/lEREREREREAcLrSX5XVxcyMzORnZ1tRj9EZBJmk0hOzCaRnJhN\nIjkxm/p59Qg9AFi7di2Sk5Nx4cIFj2O4wA6R/2nJphbf+ta3VOv/9V//ZXibHR0diprNZlMde+3a\nNUXtu9/9rqK2b98+Re3999830B2RbxnJptpCXqtXr1bUfvCDH3jVG4/XFMy0ZPP48ePdXsfExCjG\n+CJHd955p6L27//+76bvh0hGZp3TqlFbwNJbaovx/eY3v1Ed297erqi99tpr3vfgzTc3NjZi27Zt\neOqpp7xuhIjMw2wSyYnZJJITs0kkJ2bTGK8m+cuWLcOqVav4l38iyTCbRHJiNonkxGwSyYnZNMbw\n5fpbt26FzWZDeno6nE6n6jM9byouLnZ/7nA44HA4jO6236ivr7e6BQpSerIZjKqrq61ugYIUs9md\n0+mE0+m0ug0iXdk8dOiQ+3O122gCAbNJsuBx0zjDk/zq6mps2bIF27Ztw5UrV3Dx4kXk5+dj48aN\nirG3TvKDhd1u73YgIPIXPdkMRlOmTMF7771ndRsUhJjN7nr+0b+kpMS6Ziio6cnmhAkTLOjQv5hN\nkgWPm8YZvlx/5cqVOHnyJI4dO4aKigpkZWXxH5xIAswmkZyYTSI5MZtEcmI2jfN6dX0iChxNTU2K\n2siRI1XHar03Su3SqoiICEWt54rFN7W2tipqCxYsUNTGjh2rqHV1dSlqUVFRqvshktkXX3yhqJWV\nlWn+fm9X3SeiGwoLC7u99sV9wmrbjI6O1jSOiG544YUXFLWf/OQnipq/cuRpPz/+8Y8Vtddff11R\n03urgimT/OnTp2P69OlmbIqITMRsEsmJ2SSSE7NJJCdmUx+vVtcnIiIiIiIiInl4Nclva2vD/Pnz\nkZSUhJSUFOzbt8+svojIC8wmkZyYTSI5MZtEcmI2jfHqcv2lS5fi0UcfxRtvvAGXy4XLly+b1RcR\neYHZJJITs0kkJ2aTSE7MpjEhwuADBy9cuICMjAwcPXq09x2EhEj3TEO1fkJDzb1zITc3F5s3b5bu\nZ6fApyeb7777brfal7/8ZdVxZlPLxY9+9CPVsfX19Yqa2sIpHR0ditqLL76oqOXk5KCoqIjZJL/T\nk00tNT2/w+np6YrasGHDFLW//e1vmrdpNhnPFyg46Mlmfn5+t1p5ebkvW5MCs0lW0ZPNvLy8brXf\n//73quNko7ZIdEJCgqLm6d/AUzYNz2yPHz+OiIgIFBYWIjMzE0VFRbhy5YrRzRGRSZhNIjkxm0Ry\nYjaJ5MRsGmf4cn2Xy4Xa2lr853/+JyZOnIinn34apaWlKCkpUYwtLi52f+5wOOBwOIzutt9Qe/eR\nyB/0ZPPVV191f56enq76Tn4gOHfuHM6dOwcA2LJli8XdULDSk81g4HQ64XQ6rW6DSFc26+rq3J9H\nRkb6s02/YTZJFnqy+eGHH7o/t9ls/mxTSoYn+TExMYiNjcXEiRMB3LgE1tMze2+d5AcLu92OQ4cO\nWd0GBSE92ez5vN9AFR4ejvDwcABAdnY2/vKXv1jcEQUjPdkMBj3/6B+sf+wg6+nJptqtL4GG2SRZ\n6MlmamqqP1uTnuHL9W02G2JjY3H48GEAQFVVFZKTk01rjIiMYTaJ5MRsEsmJ2SSSE7NpnOGF9wDg\ngw8+wFNPPYXr168jPj4er776Ku65557uO7BwsQ6PCxGYvMieGi68R1bSms2ei334a0EStVzcfvvt\nqmPVFtT73ve+p6idPXtWUevs7FTU5syZg4ULFzKbZAmt2fQHtVvndu3aZfp+9Cx2y1ySVbRms2c+\n1RbN6i/U8qb2buihQ4eYTbKM0WweO3ZMsa377rvPl60aopatiooKRW3BggWavx/w8hF6aWlpOHDg\ngDebICIfYDaJ5MRsEsmJ2SSSE7NpjO/f0iYiIiIiIiIiv+Akn4iIiIiIiChAeDXJX7NmDSZMmIDU\n1FTk5eWp3jtLRP7HbBLJidkkkhOzSSQnZtMYw/fknz59Gr/85S/xySefYPDgwXjiiSdQUVGB/Px8\nM/vTTM/CPkSBTE82/bHAl1o28/LyFDU9/2m/8soritrDDyugj3gAACAASURBVD+sqKktiNTe3q55\nP0Rm8ua4eccddyhqV65c0bxvtcd+paSkaP5+rXgspv5ITzZ7/o6PGDFCMaalpcVnvRqlls1HH31U\nUePjn0km3mRz9OjRijFq571qq/WfOnVKtZ+YmBhFTe38NSwsTFGbOnWq6jbLy8sVtYsXL6qO1cOr\nhfc6Oztx6dIlhIaG4vLly4iKivK6ISLyHrNJJCdmk0hOzCaRnJhNYwz/eT0qKgrPPPMM4uLiEB0d\njaFDh2LGjBlm9kZEBjCbRHJiNonkxGwSyYnZNM7wJP/8+fOorKxEQ0MDTp8+jfb2dmzatEl1bHFx\nsfvD6XQa3WW/Ul9fb3ULFKSYTaXW1lYcP34cx48fx1/+8her26EgpSebROQ/zCaRnJhN4wxfrr9z\n507Ex8cjPDwcADBv3jzs3bsXCxYsUIwtLi423GB/ZbfbeV8TWYLZVBo2bBiGDRsGAPj617+OrVu3\nWtwRBSM92SQi/2E2ieTEbBpneJIfFxeHmpoaXL16FbfddhuqqqowadIkM3sjIgOszKbawj65ubmK\n2gcffKCoDRyo/t+Ry+XStO+qqipFTW2xMT2LlRGZSU82Dxw40O11VlaWV/s+f/68ovbLX/7Sq21y\nkT0KFN4cN//5z38qamoLbEVHR6t+//LlyxW1+++/X9O+1ajlEgD27t2rqO3YscPwfoj8wexzWrV8\n/OMf/9D8/R999JHhfdfV1Rn+XiMMH40feOAB5OTkICMjA2lpaRBCoKioyMzeiMgAZpNITswmkZyY\nTSI5MZvGhQhPf/IzawchIR7/qmgm2d5RyM3NxebNm/3ysxMZ4YtsevNO/pEjR1S3qfWdfDVq7+Tn\n5+fjBz/4AbNJ0goJCdH0Tr6eR+zcd999itqxY8dU962VL467zCXJTGs+pkyZoqjJ+E6+p0d66dku\nkQz88UhoWXnKJq+rIyIiIiIiIgoQfU7yFy9eDJvNhtTUVHettbUVM2fOxPjx4zFr1iy0tbX5tEki\nUmI2ieTEbBLJidkkkhOzab4+J/mFhYV4++23u9VKS0sxY8YMfPrpp8jKysKLL77oswaJSB2zSSQn\nZpNITswmkZyYTfNpuie/oaEBc+bMwYcffggASExMxLvvvgubzYbm5mY4HA588skn6jvw032/sq3o\ny3vyyR+8zWbP+/EKCwsV437wgx+ofn9//Yvqs88+i5///OfMJvmUt9ns6urqVnv//fcV46ZNm6ao\n6Xl6xOjRoxU1tfv0Pd7v54PjLnNJvuZtNo36yle+olq/9Z3Lmx599FFFbejQoYqa2irjnp6a8dxz\nzylq7e3tqmPVMJvka1Zls78z9Z78lpYW2Gw2AEBkZCRaWlqMd0ZEpmE2ieTEbBLJidkkkhOz6R31\nB1Pr1NdfT4qLi92fOxwOOBwOM3Yrtfr6eqtbIOozmw0NDe7P77nnHl+3I4Xq6mqrWyDSfdy86667\nfNwREQHB/Y4gkcyYTX0MTfJtNhvOnDnjvnxixIgRvY6/9WQlWNjtdhw6dMjqNijI6M3mqFGj/NSZ\nPKZMmYL33nvP6jYoyHh73FS7XJ+IvKc3m0TkH8ymdzRdri+E6Ha9f3Z2NjZs2AAAKC8vx9y5c33S\nHBH1jtkkkhOzSSQnZpNITsymufpceG/BggVwOp04e/YsbDYbSkpK8Nhjj2H+/Pk4deoURo0ahddf\nf111QRBAfeG9/rBwnre48B75mhnZlMnXv/511frevXsVNbVF/zo7OzXthwvvka+Zkc2Ojo5utZUr\nVyrG/eIXv1DULl68aM4PYRHmknzJyuPmzXuLezpz5ozhbfoTs0m+FGjntP7kKZt9Xq6/adMm1frO\nnTt1N+F0OoPifnwifzAzm/3B9evXMWjQIKvbIOqTWdl89913MX36dE1jXS4XBg40ZZkdooAVbMdN\nov6C2TSfX98+dzqd/twdEQWQ69evW90CkV+9++67msdqvZKFiIiIAl9gXSNPREREREREFMQ4ySci\nIiIiIiIKEH0uvOf1DoJ4IQSAC5WQvJhNZpPkFMzZZC5JZswmkZyYTSWfT/KJiIiIiIiIyD94uT4R\nERERERFRgOAkn4iIiIiIiChAcJJPREREREREFCD8MsnfsWMHEhMTkZCQgLKyMo/jGhsbkZWVhZSU\nFNjtdqxbt67X7XZ1dSEzMxPZ2dkex7S1tWH+/PlISkpCSkoK9u3b53HsmjVrMGHCBKSmpiIvLw8d\nHR3ury1evBg2mw2pqanuWmtrK2bOnInx48dj1qxZaGtr67VfItloyabeXAL+yyZzSYGK2SSSky+y\nqSWXgPZs8nyWghGz2YPwsc7OTjFmzBhx4sQJ0dHRIdLS0sTHH3+sOvbzzz8XBw8eFEIIcfHiRZGQ\nkOBxrBBCrF69WuTl5Yk5c+Z4HFNQUCDWr18vhBDi+vXroq2tTXVcU1OTGD16tLh27ZoQQojHH39c\nlJeXu7++e/ducfDgQWG329215cuXi7KyMiGEEKWlpWLFihUe+yCSjdZs6s2lEP7LJnNJgYjZJJKT\nr7KpJZdCaMsmz2cpGDGbSj5/J3///v0YN24cRo0ahUGDBuHJJ59EZWWl6tjIyEikp6cDAMLCwpCU\nlISmpibVsY2Njdi2bRueeuopj/u+cOECdu/ejcLCQgDAwIEDcffdd3sc39nZiUuXLsHlcuHy5cuI\niopyf23q1KkYNmxYt/GVlZUoKCgAABQUFOCtt97yuG0i2WjNpp5cAv7NJnNJgYjZJJKTL7KpJZeA\nvmzyfJaCDbOp5PNJflNTE2JjY92vY2Jiej0JuenEiROoq6vDgw8+qPr1ZcuWYdWqVb0+F/H48eOI\niIhAYWEhMjMzUVRUhCtXrqiOjYqKwjPPPIO4uDhER0dj6NChmDFjRq89trS0wGazAbjxS9PS0tLn\nz0UkCyPZ7CuXgPXZZC6pv2M2ieTki2xqySWgPZs8n6VgxGwqSbnwXnt7O3JycrB27VqEhYUpvr51\n61bYbDakp6dDCAEhhOp2XC4XamtrsWTJEtTW1mLIkCEoLS1VHXv+/HlUVlaioaEBp0+fRnt7OzZt\n2qSr775+CYj6s75yCciZTeaSAh2zSSQns85nAe3Z5PksUd+CIZs+n+RHR0fj5MmT7teNjY2Ijo72\nON7lciEnJwcLFy7E3LlzVcdUV1djy5YtiI+PR25uLnbt2oX8/HzFuJiYGMTGxmLixIkAgJycHNTW\n1qpuc+fOnYiPj0d4eDgGDBiAefPmYe/evb3+bDabDWfOnAEANDc3Y8SIEb2OJ5KJnmxqySUgRzaZ\nS+rvmE0iOZmdTa25BLRnk+ezFIyYTSWvJvlaVjGcNGkSjhw5goaGBnR0dKCioqLXFQoXLVqE5ORk\nLF261OOYlStX4uTJkzh27BgqKiqQlZWFjRs3KsbZbDbExsbi8OHDAICqqiokJyerbjMuLg41NTW4\nevUqhBCoqqpCUlJStzE9/5KTnZ2NDRs2AADKy8t7PcEi8iezs6kll4A12WQuqT9hNjcAYDZJPlZk\nU2suAe3Z5PksBRpmcwMAA9nUvERfD3pWzd++fbtISEgQY8eOFS+++KLHbe7Zs0eEhoaKtLQ0kZ6e\nLjIyMsT27dt77cPpdPa64mFdXZ2YOHGiSEtLE9/4xjfE+fPnPY4tLi4WiYmJwm63i/z8fNHR0eH+\nWm5urhg5cqQYPHiwiI2NFevXrxfnzp0TDz/8sEhISBCPPPKIaG1t7bVXIn8wO5tGcimEf7LJXFJ/\nwmwymyQnGbLZVy6F0J5Nns9SoGA2jWczRIhebjLoRU1NDUpKSrB9+3YAQGlpKUJCQrBixYpu44L9\nvh6D/7xEhjGb2jCb5G/MZt+YS7ICs9k3ZpOswGz2zVM2DV+ur2cVQ/H/Lz14/vnn3Z/39nHruK6u\nLsXHyZMncfLkSTz99NPuzz/44APFx63f87Of/cz9+dWrVxUfauO0mjNnjuJj1apVRv9pibxi9IkW\n/jRx4kT3x8iRIzFx4kRd/y+ofajlPTExUfHx0ksvWf3jU5DqD9kkCkZ6spmamooRI0YgNTUVqamp\nXh+79I4zc5venPsS+YPe+aYVOfLmPNWXmRvosy3fori4GADgdDrhdDrhcDj8sVu/++KLL3D27FkA\nwNWrVy3uhohuunTpEi5fvgwA+Otf/2pxN0RE1F81Nzejvb0dzc3NHp9mQUT+V1xcDKfTieLiYjgc\njoCdb2pleJKvZxXDm5P8m//ogSoiIgIREREAgGnTpuGdd96xuCMKRnqfaBEM7rzzTtx5550AgJkz\nZ2Lnzp0Wd0TBiNkkkpOebEZGRqK5uRmRkZH+ao8oaOmdb978IC8u19e7aj4AzRN8reO+9KUvaRrn\ni30TycpINq101113aRqnJ5vMMcmov2WTKFjozaaed/B9cf5p9jZ5zCRZ6c1mf8mRPzJneOE94MYj\nDZYuXYquri4sXrwYP/rRj5Q7CAkxfbEOrdvztAiD2ver/Yd98/JeI3Jzc7F582YuVEKW0JpNfygq\nKlLUfvWrXylqvujH42IkoaHMJllCpmzKiLkkq2jNZs97aPtzXtXy9umnnypqSUlJzCZZxqr5pjfU\netmzZ4/q2GnTppm+L8DLe/Jnz56t+p8BEVmL2SSSE7NJJCdmk0hOzKYxhi/XJyIiIiIiIiK5GJ7k\nNzY2IisrCykpKbDb7Vi3bp2ZfRGRQcwmkZyYTSI5MZtEcmI2jTN8uf7AgQOxevVqpKeno729Hfff\nfz9mzpyJxMREM/sjIp2YTSI5MZtEcmI2ieTEbBpneJIfGRnpfnxIWFgYkpKS0NTUpPqPvmrVqm6v\nf/jDHyrGeFo04JFHHlHUWltbFbX6+npF7dq1a6rbDA3lXQoUuPRk02z5+fmK2ve+9z1FzV8LFfXn\nBZEo8FiZTSLyTE82A+m4ovazjB8/3oJOiNQF0nFT7RwZAAYPHqyodXR0eL0/U2a7J06cQF1dHR58\n8EEzNkdEJmE2ieTEbBLJidkkkhOzqY/Xk/z29nbk5ORg7dq1up4bSkS+xWwSyYnZJJITs0kkJ2ZT\nP68eoedyuZCTk4OFCxdi7ty5Hsf99a9/dX8+ZswYb3bZb6jdPkDkL1qzGSycTiecTqfVbRAxm0SS\n0prN4uJi9+cOhwMOh8P3zfkZj5kkE2bTmBDh6WZ4DfLz8xEREYHVq1d73kFICF566aVutWC4Jz83\nNxebN2/2+HMR+ZLWbPpivz09++yziprdbjd933qEhIQwm2QJq7LZXzCXZBWt2Qz031G1ny80NDTg\nf26SV3/Mplov8fHxqmNPnz6tqOm5J9/Tz214kl9dXY1p06bBbrcjJCQEISEhWLlyJWbPnt19ByEh\nGDVqVLfavHnzFNtbs2aNkTZ6FRERoVr/4osvTN9XT5zkk1X0ZNNsajleunSpomb1JEa2gwEFB7Oz\nee+99ypqkydPVh1bWVmpv2ELMJdkBT3ZDMbf0WD9ucl6/TWbar38/ve/Vx1bUFBg+r4ALy7XnzJl\nCjo7Ow03RES+wWwSyYnZJJITs0kkJ2bTOD5LjoiIiIiIiChAcJJPREREREREFCC8nuR3dXUhMzMT\n2dnZZvRDRCZhNonkxGwSyYnZJJITs6mf15P8tWvXIjk52YxeiMhEzCaRnJhNIjkxm0RyYjb1M7zw\nHgA0NjZi27ZteO6553p9rEFDQ0O3175YSV+NL1bRv++++xS1Y8eOqY7dvHmz6fsn0kJrNs2m9gg9\nq1fSJ5KJmdnMzMxU1P70pz9p/n61FXmLiooUtevXr6t+v9oxdtu2bZr3TyQTq46bRNS7QMlmdHS0\nX/fn1Tv5y5Ytw6pVq3gSTyQZZpNITswmkZyYTSI5MZvGGH4nf+vWrbDZbEhPT4fT6ZTq2YRWcTqd\ncDqdVrdBQY7ZVGI2SQbMJpGc9GSzuLjY/bnD4YDD4fB9g37GYybJgtk0LkQYPMv48Y9/jD/84Q8Y\nOHAgrly5gosXL2LevHnYuHFj9x0E2F9d9FyuHxoaypM48jsrs3n27FlFLTw83PT9eCskJITZJL8z\nO5uzZs1S1LZv3665Hxkv12cuyQp6shmMv6PB+nOT9fprNtV6+dvf/qY6dsaMGabvC/Bikn+rd999\nF7/4xS+wZcsW5Q44yfdxR0Se+TubnOQTaWNGNjnJJzJfX9kMxt/RYP25SS79KZsyTPK9WnhPdrNn\nz1atR0ZGKmoLFixQ1GJjYxW18ePHK2qB9ocMIqOGDRtmdQtEQUNtQq/neKQ29re//a3m71c7sVA7\n7p47d05Re//99zXvh4iIqL8bOnSoX/dnyjv5ve7Awgmw1ZN82f6qRHQrX2Szq6vLL/vxFrNJMtOa\nGV/kTU8ufDHJZy5JZsF67AjWn5v6D9l+R9V6qa2tVR07ceJE0/cFeLm6PhERERERERHJw6tJfltb\nG+bPn4+kpCSkpKRg3759ZvVFRF5gNonkxGwSyYnZJJITs2mMV/fkL126FI8++ijeeOMNuFwuXL58\n2ay+iMgLzCaRnJhNIjkxm0RyYjaNMXxP/oULF5CRkYGjR4/2vgMv7w9U277aCve//vWvFbVFixap\nbnPw4MGG+9G7qJFM94dQcPBXNtVY+fuutu/vfve7itqMGTOQk5PDbJLfeZPNl156SVH74Q9/aFpv\nZlFbJyA5OVlR+/TTT1W/n7kkK+jJpuy/o576O3LkiKKm9kSchx56SFHrDz83BaZAyqba8REABgwY\n4NV2Tb8n//jx44iIiEBhYSEyMzNRVFSEK1euGG6QiMzBbBLJidkkkhOzSSQnZtM4w5N8l8uF2tpa\nLFmyBLW1tRgyZAhKS0vN7I2IDGA2ieTEbBLJidkkkhOzaZzhe/JjYmIQGxvrXvY/JycHZWVlpjXW\nHzmdTjidTqvboCDHbCo1NTXh9OnTANQvTyTyB2aTSE56sllcXOz+3OFwwOFw+KFD/+L5LMmC2TTO\n8CTfZrMhNjYWhw8fRkJCAqqqqlTvuwsmPX+hSkpKrGuGghazqRQdHY3o6GgAN+7Jf+ONNyzuiIIR\ns0kkJz3ZvHUiEah4PkuyYDaN82p1/XXr1iEvLw/Xr19HfHw8Xn31VbP6cvvf//1fRU1tsaFr164p\naj/96U9Vt6n2F6DW1lZF7dChQ4raRx99pKh9+9vfVt0PkVX8kU01DQ0NitqoUaP8sm81LpdLUfO0\n8AmRPxjN5rPPPuvjzvRTW+xn4EDlacXNP7Ddau7cuYpaZWWlOY0RGWDVcdNsnhbVHTt2rKLW2dnp\n63aIvBbo2fQVryb5aWlpOHDggFm9EJFJmE0iOTGbRHJiNonkxGwaY3jhPSIiIiIiIiKSCyf5RERE\nRERERAHCq0n+mjVrMGHCBKSmpiIvLw8dHR1m9UVEXmA2ieTEbBLJidkkkhOzaUyIUFs5R4PTp09j\n6tSp+OSTTzB48GA88cQT+NrXvob8/PzuO/DzIgOyyM3NxebNm1UXJiLyJSuzuWHDBkWtoKDA9P2o\nUcua2r6/+tWvYsGCBcwm+Z032ZTx91Wtp9BQ7y4QlPHnpMCnJ5v99XdUrW+1yVJ1dbWi9vDDD/fb\nn5v6t/6aTbVeysvLVccWFhaavi/Ay4X3Ojs7cenSJYSGhuLy5cuIioryZnNEZBJmk0hOzCaRnJhN\nIjkxm8YY/pN7VFQUnnnmGcTFxSE6OhpDhw7FjBkzzOyNiAxgNonkxGwSyYnZJJITs2mc4Xfyz58/\nj8rKSjQ0NOCee+5BTk4ONm3ahAULFpjZX79VX19vdQsUpJhNpebmZpw5cwYAcOnSJYu7oWDFbBLJ\nSU82i4uL3Z87HA44HA7/NeondXV1+OCDD6xug4jZ9ILhSf7OnTsRHx+P8PBwAMC8efOwd+9enqz8\nf3a7HYcOHbK6DQpCzKZSZGQkIiMjAdy4J//NN9+0uCMKRswmkZz0ZPPWiUSgSk9PR3p6uvv1xo0b\nLeyGghmzaZzhy/Xj4uJQU1ODq1evQgiBqqoqJCUlmdkbERnAbBLJidkkkhOzSSQnZtM4w+/kP/DA\nA8jJyUFGRgYGDRqEjIwMFBUVmdkbERlgZTZ7rnbqT2orkntayZTvnJIVvMlmWVmZorZixQqzW/TJ\nivlEstOTzZ6XADudTt836Ec8lyeZ9If5ptpxc9u2bYras88+64923Aw/Qk/zDvgIPatbIVLli2x2\ndXX5ZT9aecpfaGgos0nSUstMaWmpohaIk3zmkmQWEhKC6dOnd6v1l0m+1kfopaSkKGpHjx5lNklq\nVj5CT+sk39Mjpc+ePWv6/gEvLtcnIiIiIiIiIrn0OclfvHgxbDYbUlNT3bXW1lbMnDkT48ePx6xZ\ns9DW1ubTJolIidkkkhOzSSQnZpNITsym+fqc5BcWFuLtt9/uVistLcWMGTPw6aefIisrCy+++KLP\nGiQidcwmkZyYTSI5MZtEcmI2zafpnvyGhgbMmTMHH374IQAgMTER7777Lmw2G5qbm+FwOPDJJ5+o\n74D35FvdCgUw2bLZ2tqqqA0dOtT0/XjLynu3KDiYnU212tatWxW1r371q171rZaLu+++W3Vse3u7\nV/vSun8iM5mdTX+tl+EttWytW7dOUXv66ac1fz+RmbzNpkz35Kv9QeK5557z2/4Bg/fkt7S0wGaz\nAbjx/OmWlhbjnRGRaZhNIjkxm0RyYjaJ5MRsesfwI/RuFazv1vemvr7e6haImM3/z+l09psVkCk4\nMJtEcmI2ieTUVzaLi4vdnzscDsXjLoONoUm+zWbDmTNn3JdPjBgxwuy++j273Y5Dhw5Z3QYFGWZT\nXc//7EtKSqxrhoISs0kkJ2aTSE56s3nrJJ80Xq4vhOh2vX92djY2bNgAACgvL8fcuXN90hwR9Y7Z\nJJITs0kkJ2aTSE7Mprn6XHhvwYIFcDqdOHv2LGw2G0pKSvDYY49h/vz5OHXqFEaNGoXXX3/d4+Ja\nwXrZExfeI1+TMZtdXV1+2Y+3uPAe+ZK/srl69WpFbdmyZV71rkYt1wAwYMAA0/fFXJIv+SubauM8\n5chsHhfhCjW0DFef2yUygxnZNPN31NO2zp07p6jt379fUfvJT36iqNXW1nrVk9r/IaGhoR571bS6\nvjdkPMH3B07ySXa+nOQ7nU735fF97efWsWaM0zKWk3ySmZFJ/pEjRzB27Ng+J/lGcsRJPtENRib5\nQgiEhIT0Ock36xh3a4ZuHcdJPgWym+d1vs5Rb5P8Dz/8EKmpqQDkmOR7l3giIhV6FrnTOtYX2yQK\nFEeOHNE0jjkikpPVx02iQGBljm4++k8WnOQTERERERERBQhO8omIiIiIiIgCBO/J9zHew0SyYjaZ\nTZJTMGeTuSSZMZtEcmI2lXw+ySciIiIiIiIi/+Dl+kREREREREQBgpN8IiIiIiIiogDBST4RERER\nERFRgPDLJH/Hjh1ITExEQkICysrKPI5rbGxEVlYWUlJSYLfbsW7dul6329XVhczMTGRnZ3sc09bW\nhvnz5yMpKQkpKSnYt2+fx7Fr1qzBhAkTkJqairy8PHR0dLi/tnjxYthsNqSmprprra2tmDlzJsaP\nH49Zs2ahra2t136JZKMlm3pzCfgvm8wlBSpmk0hOvsimllwC2rPJ81kKRsxmD8LHOjs7xZgxY8SJ\nEydER0eHSEtLEx9//LHq2M8//1wcPHhQCCHExYsXRUJCgsexQgixevVqkZeXJ+bMmeNxTEFBgVi/\nfr0QQojr16+LtrY21XFNTU1i9OjR4tq1a0IIIR5//HFRXl7u/vru3bvFwYMHhd1ud9eWL18uysrK\nhBBClJaWihUrVnjsg0g2WrOpN5dC+C+bzCUFImaTSE6+yqaWXAqhLZs8n6VgxGwq+fyd/P3792Pc\nuHEYNWoUBg0ahCeffBKVlZWqYyMjI5Geng4ACAsLQ1JSEpqamlTHNjY2Ytu2bXjqqac87vvChQvY\nvXs3CgsLAQADBw7E3Xff7XF8Z2cnLl26BJfLhcuXLyMqKsr9talTp2LYsGHdxldWVqKgoAAAUFBQ\ngLfeesvjtolkozWbenIJ+DebzCUFImaTSE6+yKaWXAL6ssnzWQo2zKaSzyf5TU1NiI2Ndb+OiYnp\n9STkphMnTqCurg4PPvig6teXLVuGVatW9fpcxOPHjyMiIgKFhYXIzMxEUVERrly5ojo2KioKzzzz\nDOLi4hAdHY2hQ4dixowZvfbY0tICm80G4MYvTUtLS58/F5EsjGSzr1wC1meTuaT+jtkkkpMvsqkl\nl4D2bPJ8loIRs6kk5cJ77e3tyMnJwdq1axEWFqb4+tatW2Gz2ZCeng4hBIQQqttxuVyora3FkiVL\nUFtbiyFDhqC0tFR17Pnz51FZWYmGhgacPn0a7e3t2LRpk66++/olIOrP+solIGc2mUsKdMwmkZzM\nOp8FtGeT57NEfQuGbPp8kh8dHY2TJ0+6Xzc2NiI6OtrjeJfLhZycHCxcuBBz585VHVNdXY0tW7Yg\nPj4eubm52LVrF/Lz8xXjYmJiEBsbi4kTJwIAcnJyUFtbq7rNnTt3Ij4+HuHh4RgwYADmzZuHvXv3\n9vqz2Ww2nDlzBgDQ3NyMESNG9DqeSCZ6sqkll4Ac2WQuqb9jNonkZHY2teYS0J5Nns9SMGI2lbya\n5GtZxXDSpEk4cuQIGhoa0NHRgYqKil5XKFy0aBGSk5OxdOlSj2NWrlyJkydP4tixY6ioqEBWVhY2\nbtyoGGez2RAbG4vDhw8DAKqqqpCcnKy6zbi4ONTU1ODq1asQQqCqqgpJSUndxvT8S052djY2bNgA\nACgvL+/1BIvIn8zOppZcAtZkk7mk/oTZ3ACA2ST5WJFNrbkEtGeT57MUaJjNDQAMZFPzEn096Fk1\nf/v27SIhIUGMHTtWvPjiix63uWfPHhEaGirS0tJEenq6yMjIENu3b++1D6fT2euKh3V1dWLixIki\nLS1NfOMb3xDnz5/3OLa4uFgkJiYKu90u8vPzRUdH5r3O1gAAIABJREFUh/trubm5YuTIkWLw4MEi\nNjZWrF+/Xpw7d048/PDDIiEhQTzyyCOitbW1116J/MHsbBrJpRD+ySZzSf0Js8lskpxkyGZfuRRC\nezZ5PkuBgtk0ns0QIXq5yaAXNTU1KCkpwfbt2wEApaWlCAkJwYoVK7qNC/b7egz+8xIZxmxqw2yS\nvzGbfWMuyQrMZt+YTbICs9k3T9kcaHSDaqsY7t+/3+jmvPbpp58qagkJCe7Pi4uLUVxc3Od2bh2n\n9o8WGqrtDofc3Fxs3rxZ01giM8mWTdk8++yz+PnPf251GxSE9GRTCNHncevWY9TNsY8++mivPXz2\n2WcYN26cpn5vjn3uuedUvz516tRu+9bT5/r16xXj+npMEZGv8LhJJCdm819mzZqlqL399tsexxue\n5FPv6uvrrW6BiFRUV1db3QJRn4qLi+F0OlFcXAyHwwGHw2F1S6b75JNPVP9AT0RERErnzp3DuXPn\nNI01PMnXu2p+sLHb7Th06JDVbVAQYjZ7N2XKFLz33ntWt0FBSE82b77jreUKtP4qMTERiYmJ7td/\n/vOfLeyGghmPm0RyYja7Cw8PR3h4uPv10aNHPY41vLq+3lXzrab1XZBAfLeEgkt/yyZRsNCbTT3H\nI61jbz05MGusL/ok8iceN4nkxGwaZ/id/AEDBuCVV17BzJkz0dXVhcWLFyseA+BPN59NeKv77rtP\ndezBgwcVtYEDeecCBQbZsukL3/72txW1+Ph4RW3lypWK2u233+6Tnoj6ojebt06I1daI6Xn//Usv\nveRxAZ7e7tu7Vc9n8DY0NGDatGmqY7u6uhR99oWTfJJRMBw3ifojb7Jpt9sVtf58O/X58+d1jfdq\nZjt79mzeT0ckIWaTSE7MJpGcmE0iOTGbxhi+XJ+IiIiIiIiI5MJJPhEREREREVGAMDzJb2xsRFZW\nFlJSUmC327Fu3Toz+yIig5hNIjkxm0RyYjaJ5MRsGhciPK3S04fm5mY0NzcjPT0d7e3tuP/++1FZ\nWdntcTgAEBISYkqjRkyfPl21Pnr0aEVt1KhRilpJSYnhfefm5mLz5s0eF0Ei8pX+kE09fvjDHypq\nZWVlXm0zNDSU2SS/05PNm4va3fTkk08qtnfhwgXN+/bm993T/xU/+9nPFLWHHnpIUeu5QKAnO3bs\nYC7JEoF23PQFZpOswGz+y+TJkxW1vXv3esym4XfyIyMjkZ6eDgAICwtDUlISmpqajG6OiEzCbBLJ\nidkkkhOzSSQnZtM4U+7JP3HiBOrq6vDggw+asTkiMgmzSSQnZpNITswmkZyYTX28fjh8e3s7cnJy\nsHbtWoSFhZnRU0Doz89hpMDAbP6L0+mE0+m0ug0iANqyWVxc7P48UJ8tf/bsWZw7d87qNojceNwk\nkhOzeUNbWxva2to0jfVqku9yuZCTk4OFCxdi7ty53mwq4Njtdhw6dMjqNihIMZvdORyObhOlF154\nwbpmKKhpzeatk3wA+NWvfuXjzvxv+PDhGD58uPv10aNHLeyGgh2Pm0RyYjb/5Z577sE999zjft3Y\n2OhxrFeX6y9atAjJyclYunSpN5shIpMxm0RyYjaJ5MRsEsmJ2TTG8Or61dXVmDZtGux2O0JCQhAS\nEoKVK1di9uzZ3Xfgp9UOv/KVryhqFRUVqmPvvfdeRe0Pf/iDopafn2+4H66uT1aRLZtqYmJiFLWV\nK1eqjv23f/s3Rc3b3kNCQphN8js92exZCwZcXZ+s0h+Om1ZjNskK/sjmzYX9brV27VrVsV/+8pcV\nNbVsPPDAA4raxYsXVbd55swZRU3tkvyvfe1ritrWrVs9ZtPw5fpTpkxBZ2en0W8nIh9hNonkxGwS\nyYnZJJITs2mcKavrExEREREREZH1vJ7kd3V1ITMzE9nZ2Wb0Q0QmYTaJ5MRsEsmJ2SSSE7Opn9eT\n/LVr1yI5OdmMXojIRMwmkZyYTSI5MZtEcmI29fPqEXqNjY3Ytm0bnnvuOaxevdqsngy56667FLVd\nu3apjn3iiScUtby8PEXt2WefVdTUFnZQWzCByEoyZXPIkCGKWkpKiqKWmJio+v3BvNARBR6t2dyx\nY0e31/1lIT61BYDefvttRW3QoEH+aIdIM6uOm88995yiZrPZFLXz588ran/84x8VNU+L2Kot2kXU\nH5iZzYyMDEVtzJgxipraAnuA+jmpWm3//v2KWk1Njeo2N27cqKj9+c9/VtSuXbum+v2eePVO/rJl\ny7Bq1SqehBNJhtkkkhOzSSQnZpNITsymMYbfyd+6dStsNhvS09PhdDr5aI0e6uvrrW6BghSzqeR0\nOuF0Oq1ug4Ics9ldV1dX0P8bkByYTSI5MZvdnTt3Dq2trZrGGp7kV1dXY8uWLdi2bRuuXLmCixcv\nIj8/X/WSg2Bkt9tx6NAhq9ugIMRsKjkcDjgcDvfrkpIS65qhoMVsdhca2v1iwq6uLos6oWDHbBLJ\nidnsLjw8HOHh4e7Xx48f9zjW8OX6K1euxMmTJ3Hs2DFUVFQgKysraP/BiWTCbBLJidkkkhOzSSQn\nZtM4rxbek8lbb73l1fer3efx/PPPK2pLlizxaj9EgSw2NlZRU1tAaPv27f5oh6jfmjVrVp9jYmJi\nFDVP9yxOnjxZUTt37pymXt555x1N4zztX8vPAqgv0EcUKHJzc1Xragt8tbW1KWpqC0yrLSR99OhR\n1f1861vfUtTWr1+vqF2/fl31+4kCwfjx4xW1srIyRc3b+/97XqkGAF/60pdUx164cEFRmzFjhqL2\nzW9+U9N+bjJlkj99+nRMnz7djE0RkYmYTSI5MZtEcmI2ieTEbOrj1er6RERERERERCQPTvKJiIiI\niIiIAoRXk/y2tjbMnz8fSUlJSElJwb59+8zqi4i8wGwSyYnZJJITs0kkJ2bTGK/uyV+6dCkeffRR\nvPHGG3C5XLh8+bJZffVK7TE73i6QoEZtQRO1Z21/8MEHitrIkSNN74dIK6uyeerUKUVN7XF1vsgr\nUX+gNZs9M6K2qOWvf/1rn/TY09e+9jXV+m9/+1tF7R//+IeixrxTf+Dr4+a0adNU61OnTlXUhgwZ\noqi1t7cramFhYZr3/+677ypqM2fOVNTUFvcispLWbH700UfdXv/pT39SjHn55ZcVtTfffFNRe/rp\npw1265mnY6HWxWn1HksNT/IvXLiA3bt3Y8OGDTc2NHAg7r77bqObIyKTMJtEcmI2ieTEbBLJidk0\nzvDl+sePH0dERAQKCwuRmZmJoqIiXLlyxczeiMgAZpNITswmkZyYTSI5MZvGGZ7ku1wu1NbWYsmS\nJaitrcWQIUNQWlpqZm/9zuXLl/HFF1/giy++wN69e61uh4IUs6nkdDpRXFzs/iCygp5sfvbZZ+6P\ns2fP+rlT/zh79my3n5PIKjxuEslJTzZfeeUV98f+/fv93Kl/6DmfNXy5fkxMDGJjYzFx4kQAQE5O\nDsrKyoxuLiAMGTLEfR/V5MmTUVNTY3FHFIyYTSWHwwGHw+F+rbZOAJGv6cnmuHHj/NmaJYYPH47h\nw4e7Xx89etTCbiiY8bhJJCc92fze977X7bXaPfn9nZ7zWcOTfJvNhtjYWBw+fBgJCQmoqqpCcnKy\n0c3p4otFfNS2GR4erqh95zvfUdTa2toUtZiYGKxevdqc5oh0sDKbt99+u6Kmlg+iYORNNtUW2fPX\ngnYJCQmq9e9///uK2sGDBxU1tQX6Ojs7vW+MyCT+OG6OHTtWta62yJ5attUW2dPzf8D06dM1jauu\nrlbUpkyZonk/RGbSk80lS5Z0e632Zqvapf5PPvmkOc0a5KtjuVer669btw55eXm4fv064uPj8eqr\nr5rVFxF5gdkkkhOzSSQnZpNITsymMV5N8tPS0nDgwAGzeiEikzCbRHJiNonkxGwSyYnZNMbwwntE\nREREREREJBevJvlr1qzBhAkTkJqairy8PHR0dJjVFxF5gdkkkhOzSSQnZpNITsymMYYn+adPn8Yv\nf/lL1NbW4sMPP4TL5UJFRYWZvRGRAcwmkZyYTSI5MZtEcmI2jfPqnvzOzk5cunQJoaGhuHz5MqKi\noszqq1ezZs1S1N5++23T96O22uGtjy0gkpWZ2XzppZdU69u3b1fUfve73ylqZ86cUdSEEIra0qVL\nVfeTlZWlaZvf+ta3VL+fSCZaszlgwIBur/21kr4aT/uOjY3VVPvkk08UNafT6XVfRGby9TltTEyM\nal1rtr39P0Dr93/pS1/yaj9EZtOazV27dhnavs1m86Y9aRl+Jz8qKgrPPPMM4uLiEB0djaFDh2LG\njBlm9kZEBjCbRHJiNonkxGwSyYnZNM7wO/nnz59HZWUlGhoacM899yAnJwebNm3CggULzOyvX3E6\nnXx3gizHbCoxmyQDPdk8fPiw+/Phw4f7s02/OXv2LM6dO2d1G0Q8bvbAYybJgtnsTk82DU/yd+7c\nifj4eISHhwMA5s2bh7179wbtPzpw41L+Wy/nf+GFF6xrhoIWs6nUM5slJSXWNUNBS082ExIS/N2e\n3w0fPrzbHzCOHj1qYTcUzHjc7I7nsyQLZrM7Peezhi/Xj4uLQ01NDa5evQohBKqqqpCUlGR0c0Rk\nEmaTSE7MJpGcmE0iOTGbxhl+J/+BBx5ATk4OMjIyMGjQIGRkZKCoqMjM3jxqamryy360Uuvnjjvu\nsKATIn3ZTE1N7fa6uLhYMebq1auq3/vaa68pahEREYra6NGjNXQN3Hvvvar1n/zkJ4raY489pmmb\nRDKx8rjpC2oLeaktqsnLfkl2/sjmO++8o1pPTEw0dT/esnKRT6Ke/JHNQP2d92p1/eeffx7PP/+8\nWb0QkUmYTSI5MZtEcmI2ieTEbBpj+HJ9IiIiIiIiIpILJ/lEREREREREAaLPSf7ixYths9m63bvb\n2tqKmTNnYvz48Zg1axba2tp82iQRKTGbRHJiNonkxGwSyYnZNF+IUFsl5xZ79uxBWFgY8vPz8eGH\nHwIAVqxYgeHDh2P58uUoKytDa2srSktL1Xfgg8UMMjMzFbX333/f9P2oUfvnam5uVtRuv/12hIeH\nq44nMoMZ2Xz88ce71ebPn68YN2bMGNXvT09PV91mT2oZePPNNxU1T4tzDRs2TFF77rnnFLXbbrtN\n9fvVhISEMJvkM2Zkc/bs2d1qS5YsUYz7+te/bn7zOqhl6O9//7ui5unn7GnHjh3MJfmUlee0nra5\nYsUKw9v0Fx4zydeszGZ//t3uLZt9vpM/depUxUl2ZWUlCgoKAAAFBQV46623TGiTiPRgNonkxGwS\nyYnZJJITs2k+Q/fkt7S0wGazAQAiIyPR0tJialNEZAyzSSQnZpNITswmkZyYTe949Qi9mwL1+YJ6\n7d27F3v37gUADBxoyj8tkVf6yuahQ4fcn48YMcLX7VjG6XTyWd0klb6y+dlnn7k/Dw8P93U7ljh7\n9izOnTtndRtE3fCclsdMkhOzqS+bhmaiNpsNZ86cgc1mQ3Nzc0BPDvSYPHkyJk+eDODGPfkvvfSS\nxR1RsNGbzQkTJvipM2s5HA44HA7365KSEuuaoaCkN5vjxo3zU2fWGT58OIYPH+5+ffToUQu7oWDF\nc1olHjNJBsymkp5saprkCyG63dSfnZ2NDRs2YMWKFSgvL8fcuXONd9uHqKgoRe3//u//TN+P2qIF\nO3fuVNQeeeQRRS0yMtL0foi08Dab+/fv7/ZabVHL+++/X/V7vfmLak1NjaJ29epV1bFqf4gYPHiw\n4X0T+YPZx83f/va3msbpWYxP7bi3Z88ezd/f2NioqLW3t2v+fiIr+OOcduzYsYraxx9/7PV2iQKZ\nt9m8++67u71WW5D5n//8pznN9gN93pO/YMECTJ48GYcPH0ZcXBxeffVV/OhHP8I777yD8ePHo6qq\nCj/60Y/80SsR3YLZJJITs0kkJ2aTSE7Mpvn6fCd/06ZNqnW1d7n9zel0drtkwYyxvtgmkS+Ylc0r\nV67gjjvu0DS2pqYGDz30UJ/j9GTj5MmTiIuL63PcP/7xD6SkpGjaJrNJVjIrm2fPnu12OXtv6uvr\nYbfb+xynJxsHDx5ERkZGn+M++ugjJCcna9qmnp+JyGxWnNNevnwZQ4YM6XOclee0PGaS1czKpsvl\nMn1dNCvnkN5k09Dq+rLQsyiI1rG+2CaRzDxdJq9G7TJ7NXqycerUKU3jPvroI83bZDYpEOhZlK6+\nvl7TOD3ZOHjwoKZxerLJhfYo2Fy5ckXTOCvPaXnMpEDhcrlM36aVc0hvstmvJ/lERERERERE9C9+\nec7b0qVLAWi/1PfWcT0XUfAnLZcQE/VnhYWF2LVrF77yla8AUF9k79YM3nbbbaZkcurUqe7PT506\nhalTp3p8t+PmKuPh4eFBseI4EQDMnTsXf/nLX9wL6aldfjh69Gj350OHDu322qhbF7u96667VBe/\nvenmJcjDhw9HQkICAKCjo0MxrrOz0/35rT9TTzt27DDUM5E/LV26tM/z2YiICPfnf/vb35CVlWVK\nPonIs+985zvYs2eP+xxT7bh56+KwWuel/VWIUFte18wdBPkzDX38z0tkGLPJbJKcgjmbzCXJjNkk\nkhOzqeTzST4RERERERER+QfvySciIiIiIiIKEJzkExEREREREQUIv0zyd+zYgcTERCQkJKCsrMzj\nuMbGRmRlZSElJQV2ux3r1q3rdbtdXV3IzMxEdna2xzFtbW2YP38+kpKSkJKSgn379nkcu2bNGkyY\nMAGpqanIy8vrtoDQ4sWLYbPZkJqa6q61trZi5syZGD9+PGbNmoW2trZe+yWSjZZs6s0l4L9sMpcU\nqJhNIjn5IptacglozybPZykYMZs9CB/r7OwUY8aMESdOnBAdHR0iLS1NfPzxx6pjP//8c3Hw4EEh\nhBAXL14UCQkJHscKIcTq1atFXl6emDNnjscxBQUFYv369UIIIa5fvy7a2tpUxzU1NYnRo0eLa9eu\nCSGEePzxx0V5ebn767t37xYHDx4UdrvdXVu+fLkoKysTQghRWloqVqxY4bEPItlozabeXArhv2wy\nlxSImE0iOfkqm1pyKYS2bPJ8loIRs6nk83fy9+/fj3HjxmHUqFEYNGgQnnzySVRWVqqOjYyMRHp6\nOgAgLCwMSUlJaGpqUh3b2NiIbdu24amnnvK47wsXLmD37t0oLCwEcONRCr09/quzsxOXLl2Cy+XC\n5cuXuz06aOrUqRg2bFi38ZWVlSgoKAAAFBQU4K233vK4bSLZaM2mnlwC/s0mc0mBiNkkkpMvsqkl\nl4C+bPJ8loINs6nk80l+U1MTYmNj3a9jYmJ6PQm56cSJE6irq8ODDz6o+vVly5Zh1apVvT4y4fjx\n44iIiEBhYSEyMzNRVFTk8VncUVFReOaZZxAXF4fo6GgMHToUM2bM6LXHlpYW2Gw2ADd+aVpaWvr8\nuYhkYSSbfeUSsD6bzCX1d8wmkZx8kU0tuQS0Z5PnsxSMmE0lKRfea29vR05ODtauXYuwsDDF17du\n3QqbzYb09HQIITw+H9DlcqG2thZLlixBbW0thgwZgtLSUtWx58+fR2VlJRoaGnD69Gm0t7dj06ZN\nuvoO5mc0UuDrK5eAnNlkLinQMZtEcjLrfBbQnk2ezxL1LRiy6fNJfnR0NE6ePOl+3djYiOjoaI/j\nXS4XcnJysHDhQsydO1d1THV1NbZs2YL4+Hjk5uZi165dyM/PV4yLiYlBbGwsJk6cCADIyclBbW2t\n6jZ37tyJ+Ph4hIeHY8CAAZg3bx727t3b689ms9lw5swZAEBzczNGjBjR63gimejJppZcAnJkk7mk\n/o7ZJJKT2dnUmktAezZ5PkvBiNlU8vkkf9KkSThy5AgaGhrQ0dGBioqKXlcoXLRoEZKTk7F06VKP\nY1auXImTJ0/i2LFjqKioQFZWFjZu3KgYZ7PZEBsbi8OHDwMAqqqqkJycrLrNuLg41NTU4OrVqxBC\noKqqCklJSd3G9PxLTnZ2NjZs2AAAKC8v7/UEi0g2erKpJZeANdlkLinQMJtEcjI7m1pzCWjPJs9n\nKRgxmyo0L9GnYvv27WL8+PFi3LhxorS0tNdxCQkJYuzYseLFF1/0OG7Pnj0iNDRUpKWlifT0dJGR\nkSG2b9/eaw9Op7PXFQ/r6urExIkTRVpamvjGN74hzp8/73FscXGxSExMFHa7XeTn54uOjg7313Jz\nc8XIkSPF4MGDRWxsrFi/fr04d+6cePjhh0VCQoJ45JFHRGtra6+9EvmLmdk0kksh/JNN5pL6G2aT\n2SQ5WZ3NvnIphPZs8nyWAgmzaSybIUL0cpNBL7q6upCQkICqqipERUVh0qRJqKioQGJiYrdxwX5f\nj8F/XiLDmE1tmE3yN2azb8wlWYHZ7BuzSVZgNvv2/9q7/5iq7vuP429AbddSR1vTqyCoqCjoRaS0\nJsPEO9dhayztLDalpBB1ccmWxZqyNsvSDLam1cnsdN2yZFv9sUzc1jRip3adZNcoYreUWmU/6mwV\ni05pLVBRGAJnf/Tr/XI558I55557z+fe+3wkJvd+/Nxz3iiv+zmfe8/5nFDZtH26vpVb42n/d+rB\n97///cDj0f7Y6Tc0NKT746by8nJX94/EZTWbZvMWiQwP72uU4UjkuLq62vFtAmZYyWaseu+993R/\nzL43AG4JZ9w0GreOHTsW+LNmzRo5duxYRMbNaPUD3BJLx7Ru7Hs0tif5dm+NByCyyCagJrIJqIls\nAmoim/aNi8ZOampqRETE7/eL3+8Xn88Xjd266tSpU26XAIyppqZG/H6/1NTUiM/nS4hsNjU1uV0C\nAPn/YwIglowcN5csWeJ2SY4jm4hFiXBMayWbtif5Vm5VMHySb+Yf3Ox/isr/eV6vV1pbW90uAwnI\najatfPAWiWxGO8fFxcXS3Nwc1X0CItZvKRuPhud95EFYbW1t9AsCJLxxc6xTZgsLC03VEIlx024/\nsglVxNIxbTQybCWbthfeGxwclDlz5khjY6NMmTJF7r//fqmvr9fdCiApKSkq1/MY7ePkyZOGfQsK\nCiJdjpSXl0t9fT3XMiHqVMtmuIxqNGpLSUkxtb3q6mqpq6uLiZ8d8cVKNmOVUe1m19aIlfckxB8r\n2Rx5bDl//nzd9s6cOaNrmz17trNFRxHZhFvi7ZjWaaP93La/yU9JSZFXXnlFSkpKZGhoSNauXav7\nBwcQfWQTUBPZBNRENgE1kU37wrom/8EHH5T333/fqVoAOIRsAmoim4CayCagJrJpj+3V9QEAAAAA\ngFpsT/Lb29tl6dKlMm/ePPF6vbJt2zYn6wJgE9kE1EQ2ATWRTUBNZNM+26frjxs3TrZs2SIFBQXS\n09Mj9957r5SUlMjcuXOdrA+ARWQTUBPZBNRENgE1kU37bE/yJ0+eLJMnTxYRkdTUVMnNzZULFy64\n9o9utKKv1+t1oRLAXaplM1yxvNI4MJyb2czLy9O19fb26trOnj1repurVq3Stb355pu6tvz8fF3b\ne++9Z3o/QKRZyebI1fSNxqhZs2ZFplATQq20/cc//lHXdvMW18OFujMV4IZ4O6Y1yyjHH330kaVt\nOHJN/rlz5+TEiROyaNEiJzYHwCFkE1AT2QTURDYBNZFNa8JaXV9EpKenR8rKymTr1q2Smppq2Gf4\nJ4U+n098Pl+4u1XeqVOn3C4BCY5sGmtqanK7BCQ4M9lMBH6/X/x+v9tlAAF2xs0vf/nLUaoueoaG\nhhLynuNQF8e0n2tubpbjx4+b6pukhZHigYEBWbFihTz00EOyfv164x0kJbn2RjE0NGTYnpKSEvF9\nl5eXS319PW+ScIXq2QyXUd3JyeZOTKqurpa6urqY/dkR28xm02lunq4/ffp0XZvR6frJycnkEq4x\nm82Rx5ZGeTX6PY7WpWeROF1/YGCAbMI18X5Ma8Ts6frTpk0L+XOHdbr+mjVrJC8vL+Q/OAB3kE1A\nTWQTUBPZBNRENu2xfbp+U1OT/Pa3vxWv1ysLFy6UpKQkefHFF+XBBx90sr6whPrUdPv27bq21atX\nR7ocICpiIZuRsGLFCl2b0TcXgFuczuYXv/hFXVtnZ2e4ZQIJx0o2f/e73wU9f+KJJ3R9wv3W3uib\nub179+raWDgP8S5Rj2mNnD9/3lJ/25P84uJiGRwctPtyABFCNgE1kU1ATWQTUBPZtM+R1fUBAAAA\nAID7wp7kDw0NSWFhoZSWljpRDwCHkE1ATWQTUBPZBNRENq0Le5K/detWw1V7AbiLbAJqIpuAmsgm\noCayaZ3ta/JFRNrb2+XAgQPyve99T7Zs2eJUTY4JtfBJZWWlro2F9xBPVM9mJLDIHmKB3WwWFRXp\n2l5//XVdW7Ru0wXEG7PZfP7554OeGy28Z4XRIntf+tKXdG0nTpzQtfX19YW1byAWxMsxbahb3Rkt\nmDtp0iRdm9W1CcL6Jn/Dhg2yefNmDioAxZBNQE1kE1AT2QTURDbtsT3J379/v3g8HikoKBBN00J+\nOgEgusgmoCayCaiJbAJqIpv22T5dv6mpSfbt2ycHDhyQ3t5euXr1qlRWVsquXbt0fYffx9Pn84nP\n57O725hx6tQpt0tAgiKbo2tqanK7BCQoK9lMBH6/X/x+v9tlAJayeeXKlcDjL3zhC9EsE0g4HNMG\nszJuJmkOfCRy+PBh+fGPfyz79u3T7yApSblPXYaGhnRtKSkpju6jvLxc6uvrlfvZkVhiLZtmGdWd\nnGzuxKTq6mqpq6uL2Z8d8WGsbI5k9pr8zMxMZwp0QSy/JyF+jJXNWbNmBbX9+9//Dmt/sXJNPtmE\n22L9mDYS1+QnJyeH3G5YC+/FKq7pAD63ePHioOdGBxY/+tGPolWOoXAm9EC8uP3223Vt3/72t3Vt\nP//5zw1fn56e7nhNQCI6ffq0rdeFOhB/5ZVXdG2tra26thdeeEHXVl1dbasWANEXav6ZlpamazN6\nv7A6f3Vkkr9kyRJZsmSJE5sC4CCyCaiJbALGbEcNAAAQoUlEQVRqIpuAmsimNXwdBgAAAABAnGCS\nDwAAAABAnAhrkt/d3S2rVq2S3NxcmTdvnrz99ttO1QUgDGQTUBPZBNRENgE1kU17wromf/369bJ8\n+XL5wx/+IAMDA3L9+nXDfps3bw56/p3vfCec3QIYg9lsrly5Muj5T37yE12fb37zm4avnT59eth1\nAonGbDZH+sc//qFru+OOO3Rt2dnZhq9/6aWXdG0bNmwwtW8gEZjNppnFr4wWzXr88ccN+7722mum\n6ovWIrjjx4/Xtd24cSMq+waM2B03Y4XRYtJGd4KzyvYk/7PPPpMjR47Ijh07Pt/QuHEyceLEsAsC\nEB6yCaiJbAJqIpuAmsimfbZP1z979qxMmjRJVq9eLYWFhbJu3Trp7e11sjYANpBNQE1kE1AT2QTU\nRDbts/1N/sDAgLS0tMjPfvYzKSoqkqefflo2btwotbW1ur5vvfVW4PHMmTPt7jKmnDp1yu0SkKCs\nZPNPf/pT4HGiZLOpqcntEpCgrGQzEfj9fvH7/W6XAVjKZk1NTeCxz+cTn88XvUKjZGhoyPCSAyDa\nyGYwK+Om7Un+1KlTJTMzU4qKikREpKysTDZt2mTYt6SkxO5uYpbX65XW1la3y0ACspLNZcuWRbM0\nJRQXF0tzc7PbZSABWclmIhh5EJaoH3bAfVayOXwiEa9GXiPsxPXBgB1kM5iVcdP26foej0cyMzPl\n9OnTIiLS2NgoeXl5djcHwCFkE1AT2QTURDYBNZFN+8JaXX/btm1SUVEhN27ckOzsbNm+fbthv1//\n+tdBzz/44ANdn1/84hfhlAJgGLPZ/O53vxv0fNeuXbo+t99+e0RqBBKR2WyO9PHHH5tqC4VLyIDR\n2c2m0Wnta9eu1bWZXUU/lI6OjrBef+bMGV3bjBkzdG2PPvqoru2NN94Ia99AOOxmUzWhLoH517/+\npWvLzc0Ne39hTfIXLFggf/vb38IuAoCzyCagJrIJqIlsAmoim/bYPl0fAAAAAACoJaxJ/ssvvyzz\n58+X/Px8qaiokP7+fqfqAhAGsgmoiWwCaiKbgJrIpj22J/kXL16Un/70p9LS0iInT56UgYEB2bNn\nj5O1AbCBbAJqIpuAmsgmoCayaV9Y1+QPDg7KtWvXJDk5Wa5fvy7p6emG/d5///2g5zdXSByOhfcA\n55jN5tNPPx30fPny5bo+qampEakxHFOnTtW1tbe3u1AJYI3ZbDrthRdeiMp+gFhlNpsjF8+qqqrS\n9fnNb34TkRrNsHK7u6SkJF1bQ0ODrm3kLfWAaHJy3DRa/K6urk7X9vDDDxu+fu7cubb3k5KSYth3\ncHDQ1Datsp3a9PR0eeaZZyQrK0syMjIkLS1NHnjgASdrA2AD2QTURDYBNZFNQE1k0z7bk/yuri5p\naGiQtrY2uXjxovT09Mju3budrA2ADWQTUBPZBNRENgE1kU37bJ+uf+jQIcnOzpa77rpLRERWrlwp\nx44dkyeffNKx4mIZ9ySGW6xk8+jRo4HHWVlZUavRTU1NTW6XgATFuBnM7/eL3+93uwzAUjZramoC\nj30+X5QqjC6yCVWEk814zKeVbNqe5GdlZcnx48elr69PbrnlFmlsbJT77rvP7ubijtfrldbWVrfL\nQAKyks3FixdHuTr3FRcXS3Nzs9tlIAExbgYbeRBWW1vrXjFIaFayOXwiISKyffv2KFQYXSOz+YMf\n/MC9YpDQwslmPLIybtqe5N9///1SVlYmCxculPHjx8vChQtl3bp1pl5rtBhBJITazy9/+cuo7B9w\ng5Vsbty4Mej51atXdX2MFuZx22OPPaZr27p1qwuVAOaFM26Ga8qUKVHZDxCLwsmmm4vsGQl3zFZx\nzEfispLNkb+7Rr/LRgtTVldXm67H7IJ6RvsJtcBepDKXpEV4xm228EiUYWWS/41vfMPRfZeXl0t9\nfX3UPtAArEpKStK9CRlN8idOnBitkgwZZWjDhg26NrOT/OrqaqmrqyObUFYkBnyjAw7VDuaTkpLI\nJZRmNG6qtvJ8JDJENqE6o/HM7CQ/3N9ts5P8UPsJZyweLZtqvTMBAAAAAADbmOQDAAAAABAnxpzk\nr127Vjwej+Tn5wfaOjs7paSkRObMmSPLli2T7u7uiBYJQI9sAmoim4CayCagJrLpvDGvyT969Kik\npqZKZWWlnDx5UkREnnvuObn77rvl2WeflU2bNklnZ6duAa/ADkxeZ/DGG28Ytq9YscLU642EvEYh\nCtdPcU0+Is2JbI78/TT6fS0tLTV8/f79+3VtkyZN0rUtWLBA1/bnP/9Z1xYqKxUVFbq2+vp6w75m\ncE0+Ii1a46YVVVVVurYdO3Y4vp9wcN0vIs2JbG7bti2o7Ve/+pWu3+TJk3Vtb731lgM/QbD58+fr\n2iJxC2eyiUhzIpvPP/98UNsPf/hDU/s2GnOPHz9u2HfRokWmthmtvIR1Tf7ixYvlzjvvDGpraGgI\nHDBUVVXJ3r17HSgTgBVkE1AT2QTURDYBNZFN59n6Srujo0M8Ho+IfP5pZUdHh6NFAbCHbAJqIpuA\nmsgmoCayGZ5xTmxEtdvwqCASp0sBVo2VzZqamsBjn88nS5YsiXBF7mtqanK7BIBxU0T8fr/4/X63\nywCCjJXNAwcOBB7Pnj070uW4gmxCRWNl8/Dhw4HH06ZNi3Q5rrCSTVuTfI/HI5cvXxaPxyOXLl2S\ne+65x85m4prX65XW1la3y0CCsZrN4ZN8kehdQ+Sm4uJiaW5udrsMJBjGTT2fzyc+ny/wvLa21r1i\nkLCsZnP58uVBz4dPLOIF2YQKrGYzEb6ospJNU6fra5oWdPBfWloaWLBn586d8sgjj9irFEBYyCag\nJrIJqIlsAmoim84ac3X9J598Uvx+v1y5ckU8Ho/U1tbKo48+KqtWrZKPPvpIpk2bJr///e8lLS3N\neAcmT0l86KGHDNvb29t1be+8846ubfz48bo2VtdHPHMim0899VRQ2+uvv67rd+3aNcdrN3pfeOyx\nxwz7Hjx40NGaWF0fkRatcdOKrKwsXVtbW5vj+zFilLWBgQFd24QJE8glIsqJbL722mtBbWVlZbp+\nRnel+eCDDwy3+fe//93GTxJaqPePwcFBXVtKSoqubWhoyHCbZBOR5EQ2R36Tb3Q5TUNDg67t448/\nDqt2o5X4za7CH67Rsjnm6fq7d+82bD906FB4VY3iypUrcvfdd4/Z7/Dhw6ZPzfD7/UGnNwCxzqls\nXrp0yfB2P9GiaZqpSc3g4KDhAQmgGjfGTbP6+vrk1ltvNdXX7LhpZXxlLIabnMpma2ur4e3rjHzy\nySeGt5eNBrPjq9l+QKQ4lc2urq6QHwSM1N/fLxMmTLC0/dG88847cu+995rqG4nxdaTIf6Vtw6ef\nfmqqn5XroFhABDB2+fJlt0swxehbCADW9PX1me5rdty0Mr4yFiMeWPn2/ZNPPolgJQCG6+rqMt33\nxo0bju67paXFdN9IjK8jKTnJBwAAAAAA1jlyC72xFBYWiojIxYsXJT093bDPrFmzAo+vXLkSeH7H\nHXdErB4jo9Vope+MGTNs1QZE0/Tp06WtrU2mT58uIiIFBQW6Pr29vYHHZvMxVr/hpwXe7BsqMzdr\nOn/+fOC64uE1Wd1/RkbGqK8FVFBYWOjYeCQigUtyTp8+LTk5OY7UCCSitLQ0ufXWWwOnBBsdU86c\nOTPwuKOjQ2bOnCm33HKL4fZutjuVd6PxNRQzx+dArMjJyZGrV68GxrjMzExdn/z8/MDjDz/8ULKz\ns6Wzs3PU7Y6Vj9tuu01EPl8f7uZjFYy58F7YO0jwa3xYqASqIptkE2pK5GySS6iMbAJqIpt6EZ/k\nAwAAAACA6OCafAAAAAAA4gSTfAAAAAAA4gSTfAAAAAAA4kRUJvlvvvmmzJ07V3JycmTTpk0h+7W3\nt8vSpUtl3rx54vV6Zdu2baNud2hoSAoLC6W0tDRkn+7ublm1apXk5ubKvHnz5O233w7Z9+WXX5b5\n8+dLfn6+VFRUSH9/f+Dv1q5dKx6PJ2hVxs7OTikpKZE5c+bIsmXLpLu7e9R6AdWYyabVXIpEL5vk\nEvGKbAJqikQ2zeRSxHw2OZ5FIiKbI2gRNjg4qM2cOVM7d+6c1t/fry1YsED75z//adj3P//5j/bu\nu+9qmqZpV69e1XJyckL21TRN27Jli1ZRUaE9/PDDIftUVVVpr776qqZpmnbjxg2tu7vbsN+FCxe0\nGTNmaP/97381TdO0xx9/XNu5c2fg748cOaK9++67mtfrDbQ9++yz2qZNmzRN07SNGzdqzz33XMg6\nANWYzabVXGpa9LJJLhGPyCagpkhl00wuNc1cNjmeRSIim3oR/yb/r3/9q8yePVumTZsm48ePlyee\neEIaGhoM+06ePDlwT+zU1FTJzc2VCxcuGPZtb2+XAwcOyNe//vWQ+/7ss8/kyJEjsnr1ahERGTdu\nnEycODFk/8HBQbl27ZoMDAzI9evXg+6JuHjxYrnzzjuD+jc0NEhVVZWIiFRVVcnevXtDbhtQjdls\nWsmlSHSzSS4Rj8gmoKZIZNNMLkWsZZPjWSQasqkX8Un+hQsXJDMzM/B86tSpox6E3HTu3Dk5ceKE\nLFq0yPDvN2zYIJs3bx71vohnz56VSZMmyerVq6WwsFDWrVsnvb29hn3T09PlmWeekaysLMnIyJC0\ntDR54IEHRq2xo6NDPB6PiHz+S9PR0THmzwWowk42x8qliPvZJJeIdWQTUFMksmkmlyLms8nxLBIR\n2dRTcuG9np4eKSsrk61bt0pqaqru7/fv3y8ej0cKCgpE0zTRNM1wOwMDA9LS0iLf+ta3pKWlRW67\n7TbZuHGjYd+uri5paGiQtrY2uXjxovT09Mju3bst1T3WLwEQy8bKpYia2SSXiHdkE1CTU8ezIuaz\nyfEsMLZEyGbEJ/kZGRly/vz5wPP29nbJyMgI2X9gYEDKysrkqaeekkceecSwT1NTk+zbt0+ys7Ol\nvLxc/vKXv0hlZaWu39SpUyUzM1OKiopERKSsrExaWloMt3no0CHJzs6Wu+66S1JSUmTlypVy7Nix\nUX82j8cjly9fFhGRS5cuyT333DNqf0AlVrJpJpciamSTXCLWkU1ATU5n02wuRcxnk+NZJCKyqRfx\nSf59990nZ86ckba2Nunv75c9e/aMukLhmjVrJC8vT9avXx+yz4svvijnz5+XDz/8UPbs2SNLly6V\nXbt26fp5PB7JzMyU06dPi4hIY2Oj5OXlGW4zKytLjh8/Ln19faJpmjQ2Nkpubm5Qn5Gf5JSWlsqO\nHTtERGTnzp2jHmABqrGSTTO5FHEnm+QS8YZsAmpyOptmcyliPpsczyIRkU0DppfoC8PBgwe1nJwc\nbdasWdpLL70Ust/Ro0e15ORkbcGCBVpBQYG2cOFC7eDBg6Nu2+/3j7ri4YkTJ7SioiJtwYIF2te+\n9jWtq6srZN+amhpt7ty5mtfr1SorK7X+/v7A35WXl2tTpkzRJkyYoGVmZmqvvvqq9umnn2pf+cpX\ntJycHO2rX/2q1tnZOWqtgGrMZNNOLjUtOtkkl4hXZBNQU6SyOVYuNc18NjmeRSIim8GSNG2UiwwA\nAAAAAEDMUHLhPQAAAAAAYB2TfAAAAAAA4gSTfAAAAAAA4gSTfAAAAAAA4gSTfAAAAAAA4gSTfAAA\nAAAA4gSTfAAAAAAA4sT/AHgCmIu9LRFEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12355e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "tf_list = true_lst\n",
    "for img_num in tf_list[:len(tf_list)]:\n",
    "    window_img = np.clip(X_lst[img_num], window[0], window[1])\n",
    "    plt.figure(num=len(tf_list), figsize=(20, 20), dpi=200, facecolor='w', edgecolor='k')\n",
    "    plt.subplot(np.ceil(len(tf_list) / 5.0), 5, tf_list.index(img_num)+1)\n",
    "    plt.imshow(window_img, cmap='gray', interpolation='none')\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10a912f10>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nBaqD5ozZVD3EZbAMC/qKdB/XnjDxqZ1lr600bms4o7mFK9XZPpg3TBfdy5fF\nNC6bQFj6CqqKiAdoEyD1Uo9P/VxRaSIs+1mN9oSSlBFi6QAyam8fKr6XV5i+XBbTmPz0W/HfizQO\nCgFSN3Hl55lNt8RKE2EwwCuX0/xKPHffSHGWzvGZJ+LSAjSmHGRu4crS/u4bMRdFKOJSK3HmUyKt\nD4UAiYV6J/Cw1XcjhULYROipiDZu9CZs1QULfPOwZRVf1+9EGlvhGke7kq9qMmGbe+7LqLO+p+AF\nFOwvBgFQSSVEt1GiGp8QoHcQqYugN8uxY8D11xeiVjMZE4kbJ45TiA5WBTZvLkQfeyQSwMREsWeN\n/7qSiGR/FK3v2Ltm8fk2Tp1OFq4NegvN4KNR9t4hnzlO4Ts16hmS1ofeQaQpCHqziBSngzh5srr+\noni/eOkdRIrTT/gn1w0bSj1r/Nfl8Vw8vXD8bLbg+eO7JjkvWXxtMhneX5nvtHmzEZbptDmu9Fkw\npUa1z5CQqohjO1HPC1QHtTx+FUU9eutavF+C9xsfL+85VJZK5SljUO1UsmOEfUbdP4kCYlIHcSdA\n6sa/IhYx6ouxMbOojrJKnpywoVp5BVxuh+C/3759RouzZIlJ9KaK/Io+T/AYMEvwrVuBO+4AXve6\nwvnArqBWKvn+h31W7TMkpC7ikCT1vMCdQMeST7GAaR1Yc9rk8F9z2hy7xlbP6NvfX2GH4EsrnUya\nUpH5gu1uLWFVLe+5498JdHUZq7JvVxCHMbZSHzT2kloAvYNIq1NcXH1KczftNIVU7hspKTRTtmxj\niAto3p0zpcZ1M6jq8eUSyo1bOnV5nyk2b6v5fPly9Qd+MUCLNCMUAqSlsW2ju89PsCueV8edeD38\nQkKkQsrmTMaUTVzxvCYxpV3nTBX06dOW6rJl+Und2w3YU1b+3smkERpdXarTD2dUFy3KC42ieAAG\naJEmgkKAtCz+1XV/v+rEnhF1FpUaZoMG0ooGX1+a6aIUzJmMmd3POkv17LONqieTKakM5u0gDq/+\nn0Xqo6J4ABppSRNBIUBaCr/eu9gjxtHc2qvD9faurr8oD08YId49nqrHWe/W1/Vy+8ybpzo8bASM\na3/w7wTsqfAyltTZk2aDQoC0DEG9erCouzMdklsnUCDd3pvR3Nqrw9uGFFIvRPPaJn+/JyTOPdfM\n9oODefvD1JSpIUx9P2kl4hICsUQMi0gXgL8B8HYADoCPAPg+gHsBrADwQwDXquqPQq7VOMZAmpew\nHPlLlpSR44feAAASmElEQVSPoM2TzcL5wLWYvOFmXP9nGzCqPbh8XQL79wfK5AYificnbCx36/Om\nYGHsbVdg6V/+jvnwxhuBV18FxseBwUHAV9+akFai2SKG/xTAv6rqSgA/C+AZALcCGFbVnwGwD8C2\nmO5Fmpgwf/4wX/go0bbOQBqb5z+Oi/74t7Df6oVlJ3DwoHHd96JuHQeYPJUsvt/rk4X7rXkN3aeO\nGIf7LVuAj34UOHPGCIDduwuRwoR0KvVuJQAsAPAfIeefAbDUfX8BgGfKXB/zJonMFTOlea5Wr567\nb0RTmHKNtnbe9TOZdDSXq1wuMjTX/+23l6iOGpXxk5BGg2ZRB4nIzwL4KwBHYHYBTwD4LQDjqnq+\nr93Lqrow5HqtdwxklimTcK1cacRKydMq3UM3bET6v7+B0SPnoXflK5j6j2N44sxqbDj7SWT/+VW8\nuCqNZRc6sJ0EkknF+LiU3m/fXpPRbutWs/L/4heBK68s/10IaRGaSR2UArAWwG5VXQvgxzCqoODM\nzpm+HaiQcC1M7VMpeVpFkkno/hF85Zvn4fhxIPPkefinZy/B2Jgg+8+vQq7dgsW778B85z8BKObP\nFyxebPpPp4Fly4BNAw6ca34ZuO02o/u/7Tbg/e8HpqaK7kNIJ5OKoY8xAMdV9Qn3+D4YITApIktV\ndVJELgDwYrkOhnzGuXQ6jXQ6HcOwSEPw19T1Vtd79gDJJATA3r3AM88Aq1aZVf+LL5bmAwotnB5Y\nkTvTNja/K4nRUaC318tOmnRTK6chW7fi1B1348eJzwKO4Mc/Bk6dMkJg/37Tx/6RBCbv/Tpev/VX\ngFdeMWO9/35g3rxZeVSExEk2m0W2ETasOHRKAB4B8Db3/SCAu9zXLe65WwDcWebamDVlZFbw1dT1\nCLMJRMqIGeLmmVt7taZSng2gONPmxJ4RzS1cqfbtgzqQOqCppJ3v+8QJE13sRRmfOBE+VkJaHTRT\nnACMLeAQgKcAfA1AF4CFAIYBPAvgIQDnlbm2cU+JNIYyqZfLpUy2p6xio3CYITbQp7MvUxRV7K8K\n1j//O/mJf/phN37gJ2dUNUTo7PP1u2hR7GmiCZkrmkoI1DUACoHWImTV7nnYOI6ZsJNJR/v7fVG+\n69eHti/xGLr99qIVu194eG1PnND8DiGVtI2gGR4u8vLJ9zvtG6tlqb3yEs29daM5T88g0uJQCJC5\nIzhx+iZff2rofFbOVauKkrJ5aR2KVEcPDZuDG24wbXbtCp2k8yv9pK0DqQPqbB8sLQRTbmxrTmsK\nUzqw4nm1Fy0pfw0hLQCFAGk6wlJD5yfogF4+NH/Qrl2m/Q03mA+Hh1W1NMYgf7y9uM+qx0ZIC0Mh\nQJoOy1Lt7XVX9294Xk+g20zUITaEEt29lxPIH9ilFQLQKpWEDKGQMM7sBJxFM19DSDNDIUCaCn9l\nr/UXv6L9yQOaSlg6kDqg9sWrZrYJWFboxB5qbK5glyiLZam9vjdfsIY2AdLqxCUEYkkgVw+MGG4P\n/NHCSViQZAKWnUAq6WDsZ38JSx9/sBAHEBITcLLnPej+7xcgd+82J2+8EViwADpyAOl3mniBvj5f\nzd0yUcsVcdvkI4oX2ZAUg8VIaxJXxDCFAIkFVROpOzoK/Nw7FPNeJ4WJe2/5ydaLKB494KBPDiLz\nmWEk7v4LE4DW3188aVeTdqIM+fu5Y8tkTDI7QloNCgHSdFiWmbcPHTJRvrt3A29/e+VJdnwcWLHC\nLNJTCRtjzoVYOvgxYGjITP45G92vTxYm/zpz/ZTLb0RIq9FMuYMIAQC89BLwxBNmnh4ZAd7xDrPq\nLpcvyLJMegnbBgBFb+IxdG//GLB7N5x9WWxOO1i+TJG+9BXThy9PUa2E5TcipJPhTqDDiFO1EsRT\nCR04YO6jahbtY2PABReUtv/e94DVq/NX4+k3XoPVn/8UAGDyN7Zj+ff3wUIKKUxj7KY/wdIv7DRq\nojpzSzXyGRAyW3AnQKqm5oyeERExOvaxMbNg9+553XXh91q1CujqMu+7FgCX3PMpk5gum0X3qSPo\nW/OaWbGvGEf3H33aJKyLIblglII2hHQKFAIdxMmTpRk94yaRMKv+e+81KhfV8vdKJEzmz8OHgZdP\nCxJXpM1Ef8cdkE98HJknz8PYvQeQfe1yCCuBEdIQKAQ6iNnUh19wQbR7pVI+43E2ayZ6d8JPZPZi\n6R/8X8g/7jH1APbsAbZtq8smQAgpJo56AqRF8NQ1s6EPVy19VbyfbZsJ3tP5p9Pm+JFHCvn/02lj\ncWYhGEJig4ZhMiO1GFJrcsWsJQCMkA6FhmEyK9RqTJ5R9RRU6YRN+BQAhDQcCgFSkVqNyX5PoXyq\nB48KdYoJIbNLbEJARBIi8m8i8oB7fL6IPCQiz4rIt0SkK657kdmjJmOyO5nnXTGnp0rbeHWKh4bM\n3x07uPInZA6IcyfwmwCO+I5vBTCsqj8DYB+AbTHei8wSFVf0YQRX+Xv3AosWmb9AYdXf3593B63k\n/+84xr5AsxEhjSEWISAiywH8AoC/8Z2+BsAX3PdfAPC+OO5FZp9ywVVhE7QjSUze/IfQD7ir/Ouv\nBz73OfPXv+rfv7/IHTTM/7/RwW2EEMRWaH4PgEsBbALwgHvudKDNy2WurTuvNpl9woq9FJ1b8bza\nkELVL39lsYj1AMoVrieExFdPoO44ARH5RQCTqvqUiKQryZtyHwwNDeXfp9NppGNIDUAaS5jB2HFM\n3iDbBkZfWIaTN92Fpbt3AuedV7zqT6dL/f8feaTEJuDZI7y0z0z2RjqZbDaLbCMi5uuVIgB+H8Ax\nAD8AcALAawD+AcBRAEvdNhcAOFrm+oZJStI4guUhLcv8FXEUsLV/9WlTwWt4WHX+/Hy9YM1kVNev\nN68IlcGC9YUJIQY0Y2UxEdkE4CZVfa+I7ATwkqreJSK3ADhfVW8NuUbjHAOZPfxBZC++6A8OUxw/\nLoXMoVNThVU/YLYK+/cb+8DWrWZ3EEN2UEI6iVYIFrsTwLtF5FkA73SPSRvhNxgXu5JKcXSwXwAA\nRu2TTkfyDiKENBamjSCxUVV6iWyWOwFC6oDlJcmcUEseoZJrvFiCHTvMxJ/NmmRxTA5HSGQoBMis\nU0uR9rLXMFkcIXVBIUBmnVoyg7KwOyGNoRUMw6SFCYsGriWPEAu7E9LccCdASghV4ahR1+T1+4ts\nSCqa+oaF3QmJH+4ESMMoiQbOuYbcvXuNW+jRLKQ/eupnFnYnpHmhEOhAZsrM2d0N9PUqUrDQt+oV\ndL8+CVx3HXDVVcD27Uz9TEgbQSHQYVTKzOkJBwDIZAVj9z2G7PhbIXcMAb/3e8Cv/qrJCMrgLkLa\nBgqBDqNcpbCgcACApb+yAfJxN6r3F38R+PrXK6Z+JoS0HhQCHUY5b51Q4ZDNmgn/9tuBL34RuO02\nUxNgzx4T3MVykIS0PPQO6kDCvHVs2xT7OnTICId9D9s41fsedP/hpyGb06Yy2Gc/W4jqtQPeQvT8\nIWRWYbAYiQ1PFXTgAHD55cC3vw28613A6Kiir0/KRvnWEkFMCIkHCgESG8Go3iefBC67bOYoX0YD\nEzJ3ME6AhFJLYfagnWDVqmhRvowGJqT14U6gjSinnvHr7VXDdfhB3X5UXT9tAoTMDVQHkRLC1DNL\nlhQLBlXg4EHq8AlpdZpGCIjIcgB/D2ApAAfAX6vqn4nI+QDuBbACwA8BXKuqPwq5nkIgJlSNj783\n4WezwbKPpo1tU4dPSKvTTELgAgAXqOpTIjIfwHcAXAPgwzA1hneyxvDsEVTPBAWDfyeQzVKFQ0ir\n0jRCoKRDkfsB/IX72qSqk66gyKrqxSHtKQRiJExHH7QJTE6az5jUjZDWpSm9g0TkjQAuBfAogKWq\nOgkAqpoDQN+RBlMuL1Awi+f11wMXXVSaO4gQ0nmk4urIVQX9I4DfVNXXRCS4vC+73B8aGsq/T6fT\nSDM5WU2EpX4I6vyjtCGENB/ZbBbZBuTsikUdJCIpAP8C4Buq+qfuuaMA0j51UEZVV4ZcS3VQTKgC\n6U2K0YNS0Pk7xVG+YcZjqoQIaT2aTR30twCOeALA5QEAH3Lf3wDgn2O6FymDODYyUxswdu8BZLOA\nZrKYXPceqFVI9CZiXEPHxigACCHxeAdtAPBtAIdhVD4K4DMAHgfwVQAXAXgBxkX0lZDruROIk2wW\n2LIFzo0fx+bffzdGtQd9GxKMCSCkzWha76CqB0AhED9DQ5i84/9heWIClpNEKgUcO2aEACN7CWkP\nmk0dRJoFtwZA9/aPoS/xGFJJB729xiMorJoYIaSz4U6gnbDdgvA7dgDpNJx9WZy8eSf0gQdx0RuT\nzPZJSBtBdVAHEilZWyDnP2wbmkjSI4iQNoNCoMOot4ALs30S0l5QCHQYLOBCCPFDw3CHwQIuhJBG\nwJ1AC0GVDiHEg+ogQgjpYKgOIoQQUjcUAoQQ0sFQCLQatl35mBBCqoBCoJXwIoK9nOLZrDmmICCE\n1EhsRWXILJBMmpQQW7YAW7cCu3cDe/YURwgTQkgVcCfQaqTTRgDccYf5yypshJA6oBBoNdwsoRgc\nNH8bUG6OENI5ME6glQhkCUU2C2zbBoyMUCVESIfRMsFiInIVgD+B2XXco6p3BT6nEKiGkCyhFACE\ndB4tIQREJAHg+wDeCWACwCEA16vqM742FAKEEFIlrRIxvA7Ac6r6gqpOA/gKgGsafE9CCCERabQQ\nWAbguO94zD1HCCGkCWiKOIGhoaH8+3Q6jTTdHgkhpIhsNotsA7wBG20T6AEwpKpXuce3AlC/cZg2\nAUIIqZ5WsQkcAvAWEVkhIvMAXA/ggQbfkxBCSEQaqg5SVVtEPg7gIRRcRI828p6EEEKiw2AxQghp\nQVpFHUQIIaSJoRAghJAOhkKAEEI6GAoBQgjpYCgECCGkg6EQIISQDoZCgBBCOhgKAUII6WAoBAgh\npIOhECCEkA6GQoAQQjoYCgFCCOlgKAQIIaSDoRAghJAOhkKAEEI6mLqEgIjsFJGjIvKUiNwnIgt8\nn20Tkefcz6+sf6iEEELipt6dwEMALlHVSwE8B2AbAIjIKgDXAlgJ4GoAd4tI3cUP2p1GFJFuVfgs\nCvBZFOCziJ+6hICqDquq4x4+CmC5+/69AL6iqpaq/hBGQKyr516dAH/gBfgsCvBZFOCziJ84bQIf\nAfCv7vtlAI77Pht3zxFCCGkiZiw0LyIPA1jqPwVAAdymqg+6bW4DMK2qX27IKAkhhDSEugvNi8iH\nAPw6gCtU9Yx77lYAqqp3ucffBDCoqo+FXM8q84QQUgNxFJqvSwiIyFUA/gjAgKq+5Du/CsAXAayH\nUQM9DOCtWq/EIYQQEiszqoNm4M8BzAPwsOv886iq3qiqR0TkqwCOAJgGcCMFACGENB91q4MIIYS0\nLg2NGBaRq0TkGRH5vojcUqbNn7lBZU+JyGW+8z8Uke+KyJMi8ngjxzkbzPQsRORnRGRURH4iIp+q\n5tpWo85n0Wm/iw+63/e7IjIiImuiXttq1PksOu138V7/9xWRDVGvLUFVG/KCETD/DmAFgJ8C8BSA\niwNtrgbwdff9ehh1kvfZDwCc36jxzeYr4rNYDOAdAD4H4FPVXNtKr3qeRYf+LnoAdLnvr/L+j3To\n7yL0WXTo7+Js3/vVAI7W+rto5E5gHYDnVPUFVZ0G8BUA1wTaXAPg7wFAjedQl4h47qiC9sltNOOz\nUNVTqvodAFa117YY9TwLoPN+F4+q6o/cw0dRiLfpxN9FuWcBdN7v4r98h/MBOFGvDdLIhxYMGBtD\nacBYpaAyhTE4HxKRX2/YKGeHKM+iEdc2I/V+n07+XfxvAN+o8dpmp55nAXTg70JE3iciRwE8CBOs\nG/laP/V6BzWSDap6QkSWwPzjHlXVkbkeFJlzOvJ3ISKbAXwYwMa5HstcU+ZZdNzvQlXvB3C/iGwE\n8LsA3l1LP43cCYwDeIPveLl7LtjmorA2qnrC/XsSwD+htXMPRXkWjbi2Ganr+3Ti78I1gP4VgPeq\n6ulqrm0h6nkWHfm78HCF3ZtFZGG113odNMq4kUTBQDEPxkCxMtDmF1AwDPegYPQ6G8B89/05AA4A\nuHKuDTaNfBa+toMAbqrl2lZ41fksOu534f6Hfg5AT63PsRVedT6LTvxd/LTv/VoAx2v9XTT6y1wF\n4Fn3H+5W99xvAPior81fuIP+LoC17rk3uYN/EsBh79pWfs30LGDyMx0H8AqAlwEc8/2wS65t5Vet\nz6JDfxd/DeAlAP/mfu/HK13byq9an0WH/i4+DeB77rM4AKC31t8Fg8UIIaSDaReXKkIIITVAIUAI\nIR0MhQAhhHQwFAKEENLBUAgQQkgHQyFACCEdDIUAIYR0MBQChBDSwfx/XsAMtQSw+dIAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a604d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_scatter = []\n",
    "X_num_lst = []\n",
    "for img in X_lst:\n",
    "    mm = np.mean(np.clip(img, 0.1, 0.3))\n",
    "    m1 = np.count_nonzero(np.clip(img, 0.1, 0.3) - 0.1)\n",
    "    X_scatter.append(mm)\n",
    "    X_num_lst.append(m1)\n",
    "\n",
    "X_plt_true = []\n",
    "X_num_plt_true = []\n",
    "\n",
    "for item in true_lst:\n",
    "    img = X_scatter[item]\n",
    "    X_plt_true.append(img)\n",
    "    num = X_num_lst[item]\n",
    "    X_num_plt_true.append(num)\n",
    "    \n",
    "plt.scatter(X_plt_true, X_num_plt_true, marker='x', color='r')\n",
    "\n",
    "X_plt_false = []\n",
    "X_num_plt_false = []\n",
    "\n",
    "for item in false_lst:\n",
    "    img = X_scatter[item]\n",
    "    X_plt_false.append(img)\n",
    "    num = X_num_lst[item]\n",
    "    X_num_plt_false.append(num)\n",
    "    \n",
    "plt.scatter(X_plt_false, X_num_plt_false, marker='.', color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lss=[]\n",
    "for item in X_lst:\n",
    "    it = item.flatten()\n",
    "    lss.append(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=784.759970351, cache_size=100, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.000695192796178,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''SVM play'''\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "X_svm = lss\n",
    "y_svm = y\n",
    "clf = svm.SVC(C=784.75997035146224, cache_size=100, class_weight=None, coef0=0.0,\n",
    "  decision_function_shape=None, degree=3, gamma=0.00069519279617756048, kernel='rbf',\n",
    "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
    "  tol=0.001, verbose=False)\n",
    "\n",
    "clf.fit(lss, y_svm)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters are {'C': 1.0000000000000001e-05, 'gamma': 1e-10} with a score of 0.76\n"
     ]
    }
   ],
   "source": [
    "C_range = np.logspace(-5, 10, 20)\n",
    "gamma_range = np.logspace(-10, 3, 20)\n",
    "param_grid = dict(gamma=gamma_range, C=C_range)\n",
    "cv = StratifiedShuffleSplit(n_splits=5, test_size=0.3, random_state=42)\n",
    "grid = GridSearchCV(svm.SVC(), param_grid=param_grid, cv=cv)\n",
    "grid.fit(X_svm, y_svm)\n",
    "\n",
    "print(\"The best parameters are %s with a score of %0.2f\"\n",
    "      % (grid.best_params_, grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
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